Some statistical properties of the
ambient noise in the Baltic Sea and
its relation to passive sonar
Johan Fridström
A thesis presented for the degree of
Master of Science
Royal Institute of Technology
Sweden
2015
This thesis is part of an EU project financed by LIFE+
I
Abstract
The Baltic Sea Information on the Acoustic Soundscape (BIAS) is an European Union
financed research project coordinated by FOI. The goal is to determine the soundscape
of the Baltic Sea. This study is a part of BIAS and was focused on generating Wenz
curves for the Bothnian Sea, which is a part of the Baltic Sea. Wenz curves describe
the spectral noise level at different sea states. The investigation of the soundscape was
done for both summer and winter conditions when the hydrographical situations differ.
Further investigations of the noise dependencies of the natural and anthropogenic sound
sources were performed. Wind and ships were dominating in a broad frequency band.
The influence of ship noise on the ambient noise is dependent of frequency and distance.
Ships within 5 km distance dominates the recorded noise levels and are not part of the
ambient noise. At distances longer than 5 km a single ship becomes non-distinguishable
and part of the range independent noise floor.
Passive sonar ranges were calculated for two different sources. The range was shown to
be clearly dependent on the sea state. With an increase of wind speed from sea state 0.5
to 3 the range increased with about 100%.
The results of this study will be used in BIAS and in related research projects. It may
be used for marine biologics but also for development of sonar and underwater systems.
II
Sammanfattning
Statistisk beskrivning av Östersjöns ljudlandskap – och dess påverkan på räckvidderna för passiva hydrofonsystem
BIAS är ett EU finaniserat projekt som koordinaeras av FOI och syftar till att beskriva
ljudlandskapet i Östersjön. Denna uppsats är en del av BIAS med fokus på att generera Wenzkurvor för Bottenhavet, vilket är en del av Östersjön. Wenzkurvor beskriver
spektrala ljudegenskaper för olika väderlekar. Kurvorna är framtagna för både sommaroch vinterförhållanden. De dominerande ljudkällornas inverkan på ljudbilden studerades.
Resultaten visar att vind och fartyg är de dominerande faktorerna.
Fartygens bidrag till bakgrundsljudet visade sig bero på både frekvens och avståndet till
mätpunkten. Fartyg innanför en radie på 5 km dominerade de uppmätta ljudnivåerna.
Utanför denna radie kunde inte enskilda fartyg med säkerhet idenfieras i ljuddata. Fartygens ljud försvann in i trafikmullret som ständigt finns i Bottenhavet.
Utifrån de olika hydrografiska karaktärerna beräknades räckvidden för två ljudkällor för
en passiv sonar. Räckvidden var klart beroende av väderförhållandet. Med en ökad vindhastighet från sjötillsånd 0.5 till 3 ökade maximala detektionsavståndet för sonaren med
ungefär 100%.
Resultaten från den här studien kommer användas inom BIAS. De kan också komma
att användas av marinbiologer inom forskning på djurlivet i Östersjön men kan även
användas för utveckling av sonarsystem och andra undervattenssystem.
III
Preface
The work of this thesis was carried out at Totalförsvarets Forskningsinstitut in Kista,
Stockholm. The task was a part of BIAS but also supported by FOI Underwater department. Professor Peter Sigray led the work with good help from PhD Leif K.G. Persson.
First I want to thank the entire Underwater department at FOI for all interesting discussions, nice coffee breaks and a very pleasant stay. Extra thank to PhD Jörgen Pihl who
helped me with sonar calculations. Also PhD Mats Nordin has earned extra gratitude for
without any doubt recommended me for this job and for all good and guiding discussions
during my entire study time at KTH.
I want to send special thanks to Professor Jakob Kuttenkeuler at KTH for the encouragement and enthusiastic support during the work and MsD Sebastian Thuné for all the
profitable discussions.
I am most grateful for the help I got from Professor Peter Sigray and PhD Leif K.G.
Persson who have helped me daily by answering question, provided me with good literature, discussed solutions and results but most of all always prioritized my time before
their own making the time at FOI in Kista a very stimulating and funny period of my life.
Of course I also want to thank my parents, Inger and Håkan, who always and doubtless
supported me and made it possible to complete the Master Degree in Science. Also my
girlfriend Sandra owns my gratitude for all the positive support.
Stockholm June 2015
Johan Fridström
IV
Contents
1 Glossary and abbreviation
1
2 Introduction
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3 Goals and structure of this thesis
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4 Limitations
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5 Theory Part I: Underwater acoustics
5.1 Basic acoustic properties . . . . . . . . . . . . . . . .
5.2 Relevant sources of noise in the Baltic Sea . . . . . .
5.2.1 Sound propagation, refraction and absorption
5.3 Ambient noise . . . . . . . . . . . . . . . . . . . . . .
5.3.1 Rule of fives . . . . . . . . . . . . . . . . . . .
5.3.2 Acoustics of the Baltic Sea . . . . . . . . . . .
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7 Theory Part III: Passive sonar
7.1 Purpose and use of passive sonar . . . . . . . . . . . . . . . . . . . . . .
7.2 Passive sonar equation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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8 Method
8.1 Data collection . . . . . . . . . . . . . . . .
8.1.1 Noise recordings . . . . . . . . . . . .
8.1.2 Meteorological data . . . . . . . . . .
8.1.3 AIS data . . . . . . . . . . . . . . . .
8.2 Signal processing . . . . . . . . . . . . . . .
8.2.1 Pre-processing . . . . . . . . . . . . .
8.2.2 Grubbs’ test . . . . . . . . . . . . . .
8.2.3 Kolmogorov-Smirnov two sample test
8.2.4 Averaging . . . . . . . . . . . . . . .
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6 Theory Part II: Signal processing and analysing
6.1 Stationarity . . . . . . . . . . . . . . . . . . . . .
6.2 Outliers . . . . . . . . . . . . . . . . . . . . . . .
6.3 Correlation . . . . . . . . . . . . . . . . . . . . .
6.4 Spectral analysis . . . . . . . . . . . . . . . . . .
6.5 Fourier analysis . . . . . . . . . . . . . . . . . . .
6.6 Power Spectral Density . . . . . . . . . . . . . . .
6.7 Bandwidth . . . . . . . . . . . . . . . . . . . . . .
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8.3
8.4
8.5
Handling of different data sets . . . . . . . . . . . . . . . . .
8.3.1 Combining ambient noise and meteorological data . .
8.3.2 Combining ambient noise and shipping data . . . . .
Method of determining ambient noise and its
dependencies . . . . . . . . . . . . . . . . . . . . . . . . . .
8.4.1 Transformation from time to frequency plane . . . . .
8.4.2 Correlation of wind, waves and ambient noise . . . .
8.4.3 Wenz curves based on wind speed . . . . . . . . . . .
8.4.4 Ambient noise dependency of significant wave height
8.4.5 Ambient noise dependency of hydrography . . . . . .
Sonar range calculations . . . . . . . . . . . . . . . . . . . .
9 Results and discussion
9.1 Signal processing results . . . . . . . . . . . . . . .
9.2 Meteorological conditions at the measuring location
9.3 Ambient noise in different meteorological conditions
9.4 Shipping and ambient noise . . . . . . . . . . . . .
9.5 Range of passive sonar . . . . . . . . . . . . . . . .
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10 Conclusions
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References
61
A About the project
A.1 BIAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A1
A1
B The location
B.1 Weather at the position . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.2 Hydrography of the location . . . . . . . . . . . . . . . . . . . . . . . . .
A2
A2
A4
VI
1
Glossary and abbreviation
Ambient noise
Ambient noise is the noise background that is observed with a non-directional hydrophone
excluding self-noise or identifiable localized source of [22]. In total absence of anthropogenic sounds the term natural ambient noise is used [23].
Anthropogenic
Means that (in this case) noise has its origin in the influence of human activity.
Bandwidth
Bandwidth is the range between frequency upper and lower frequency content of a signal.
It is measured in Hz [23].
Noise
Noise is sound of random nature, which means that the spectrum contains no clear defined frequency components. Noise can also refer to unwanted signals. What is regarded
as noise depends on the receiver and the context. [23].
Power Spectral Density:
A power representation of a signal with the amplitude energy/frequency. Often used for
stationary random signals [20].
Octave
An octave is a doubling of frequency. Octave band is a frequency band with the mid
frequency determining the name [25].
Refraction
The bending of sound due to environmental changes in the medium [5].
1
Root mean square
The squared mean value of the signal. It is often used to describe a quantity of a signal
with both positive and negative values [1].
Sea States
Sea states is defining different weather conditions at sea. It is ranged from zero to eight
based on wind speed and significant wave height [5].
Sound
Acoustic energy radiated through a medium from an object that vibrates. It can be either
desired signals or noise [23].
Sound pressure levels
The acoustic pressure relative the reference pressure 1 μPa squared measured in a logarithmic scale. Often used to express sound with a quantity [20].
Stationary
A signal whose statistical properties does not change with time is stationary [20].
Transient signal
A signal with a limited duration and a clear start and stop [25].
2
AIS
BIAS
CDF
DFT
DSP
FMV
FOI
HELCOM
HIRLAM
LOFAR
PSD
PSU
RMS
SMHI
SOFAR
SONAR
SPL
SS
Automatic Identification System
Baltic Sea information on the Acoustic Soundscape
Cumultative Distribution Function
Discrete Fourier Transform
Digital Signal Processing
Försvarets Materiellverk (Swedish Defence Material Administration)
Totalförsvarets Forskningsinstitut (Swedish Defence Research Agency)
Helsinki Commission, Baltic Marine Environment Protection Commission
High Resolution Limited Area Model
Low Frequency Analysis Recorder
Power Spectral Density
Practical Salinity Unit [g/kg = ppt]
Root Mean Square
Sveriges Meteorologiska och Hydrologiska Instut
(Swedish Meteorological and Hydrological Instute)
Sound Fixing and Ranging
Sound Navigation and Ranging
Sound Pressure Level
Sea State
3
2
Introduction
The Element of Surprise is an effective tactic in warfare which was described in the Liad
by Homeros. In the marine environment covert vessels will undoubtedly have a point
of advantage. The Swede Torsten Nordenfelt realized this fact and in 1883 he was the
first person to build and design a steam engine driven torpedo-carrying submarine [27].
Meanwhile the political arena of Europe got more and more infected by conflicts and in
the beginning of the 20th century became the start of a massive armament.
Submarines were used in naval battles for the first time in history. As a response, new
strategies were developed to detect and to combat the submarine threat. It became
important to acquire knowledge of the acoustic underwater environment. From a naval
point of view it was important not only to address the sources (sound produced by submarines) but also to understand the properties of the ambient noise. The ability to “hide
and seek” is strongly linked to these two properties. The Naval activities were however
classified and not open to the general public.
The Russians developed early a tool that used radio waves and hydro acoustics to determine the distance to other ships. Their results were published almost simultaneously as
the British physicist Joly presented his method for determining distance and direction to
underwater sound sources. [7]. The Russian results were not recognized and the literature
today is based on results achieved by research performed by researchers in the western
countries. The development continued and during the Second World War the listening
devices were further developed. This in combination with an increased research in hydro
acoustics resulted in a better understanding of the underwater sound environment. Post
Second World War a collection of papers written by researchers in the United States
about hydro acoustic behaviour were presented. This collection, Physics of Sound in the
Sea [2], became the keystone in the following development in the hydro acoustic field.
The civilian society regarded the underwater environment as silent, not at least highlighted by the documentary movie The Silent World produced by the oceanographers
Jaques- Yves Cousteau and Louise Malle. The general public awareness of underwater
sound was raised with the observation of the stranding whales, correlated with sonar
activities [6]. Presently, the awareness of the sound as a potential “pollution” is growing.
4
One of the most fundamental scientific investigation on underwater noise was done by
Wenz (1962). He showed that the ambient noise in water depends on many different
factors. He summarized in a graph the variety of noise sources and their contribution
to the ambient noise. This graph has been in use since then and is commonly known as
Wenz curves. His graph is shown in Fig. 2.1 and has been supplemented with hearing
ability for some species in the Baltic Sea.
Figure 2.1: Spectral sound levels in deep ocean adapted from the Wenz curves [26][18].
Including anthropogenic and natural sources. The graph also shows the range of hearing
thresholds for some animals of the Baltic Sea.
5
The graph of Wenz is still valid. It is used by researchers in hydro- acoustics even though
the research is based on measurements undertaken in the years before 1962. The “silent
world” has however changed during the past fifty years. There are strong indications that
the noise levels have increased [9]. An increased density of commercial shipping explains
part of this change but also the introduction of new types of propulsion systems. Further,
the number of infrastructures at coastal and offshore areas has increased, compared to
the levels in the early sixties.
The research underlining the Wenz curves was based on noise in “deep” oceans. The
Baltic Sea is a shallow sea and the applicability of the Wenz curves in the Baltic Sea can
therefore to some degree be questioned. In his paper it is stated that a rise of the noise
intensity of 2-3 dB is expected in shallow waters. It should be underlined that he defined
shallow as less than 100 m. Only a minor part of the Baltic Sea is deeper than 100 m, the
actual average depth is 54 m. His forecast has been shown to be valid in the deep oceans.
The Baltic Sea is a brackish sea where a strong thermocline develops during summer and
it has a complex topography, which differs from the environments that Wenz results were
based on. This is one of the motivations for carrying through a study of the Ambient
Noise of the Baltic Sea. One of the aims is to present an update of Wenz curves valid for
the Baltic Sea. However the generated Wenz curves would only be valid in peace time.
A military conflict in the Northern Europe would probably reduce the shipping in the
Baltic Sea which would result in a decrease of ambient noise levels.
As was eluded earlier, anthropogenic generated sound might have a negative impact on
the marine life. The focus of this thesis is to better understand the ambient sound and its
role in the marine environment. Thus, the same result can be used both in environmental research and for development of underwater systems such as submarines and sonar
systems. The work undertaken herein was a part of the Baltic Sea Information on the
Acoustic Soundscape project (BIAS). The aim of BIAS was to establish the underwater
soundscape in accordance with the Marine Strategy Framework Directive, Descriptor 11,
that declares that the member states of the European Union have to establish the baseline
of sound levels before 2016 [23]. The Swedish Defence Research Agency (FOI) is coordinating the project and a more detailed presentation of BIAS is appended in Appendix A.
6
3
Goals and structure of this thesis
This thesis has two goals. The first is to develop tools for characterizing the ambient
noise. These were employed on data that were obtained in the Bothnian Sea. The second
goal is to quantify the detection range of sound sources, based on the results from the
first part.
The structure of this thesis follows the goals. In chapter 4 limitations of the study is
presented and is followed by the basic theory of hydro-acoustics which is introduced in
chapter 5 and it begins with a general description of acoustics. A presentation of sound
sources is given. The Wenz curves are introduced and specific properties of the unique
acoustic environment of the Baltic Sea, and the Bothnian Sea in more detail, are discussed. In chapter 6 signal processing theory is presented. The chapter starts with an
introduction of stationarity followed by explanations of outliers, correlation and spectral
analysis in text and illustrative examples. Chapter 7 contains a presentation of the sonar
concept. The passive sonar equation and the use of sonars is described. Chapter 8 consists of a comprehensive description of the methodology. The results are presented and
discussed in chapter 9. In the final chapter, chapter 10, conclusions are made and an
outlook is given.
All research have been performed for the Bothnian Sea, well away from the coastline and
shipping lanes. The following main topics have been investigated:
• Correlation of wind speed, wave height and ambient noise levels.
• Parametrization of the ambient noise.
• Establishment of the Wenz curves.
• Determination of the cumulative range-distribution.
• Establishment of detection ranges as function of frequencies and meteorological
conditions for passive sonar.
7
4
Limitations
The recordings of the sound data were done with a sampling frequency of 32000 Hz. The
Nyquist theorem restricts the analysis to frequencies lower than 16000 Hz. The lower
limit of the bandwidth was set by the hardware of the autonomous recorder to 10 Hz.
Limitations in battery and storage capacity only allowed recordings of 23 minutes every
hour. The obtained results are not complete and thus associated with statistical errors.
The results presented in this thesis are based on data from Bothnian Sea. The hydrophone
was located within 20 km from a shipping line. There were not enough of recordings with
no ships within a 20 km radius to statistically determine the natural ambient noise.
At the location of the hydrophone some sea states never occurred. The sediment characteristics are also unknown. The meteorological data used were model based and not
obtained from measurements. However, it was provided by SMHI and can for this study
be regarded as reliable.
8
5
Theory Part I: Underwater acoustics
Underwater acoustics is a broad discipline that encompasses many different applications.
Here the study will be restricted to underwater phenomena that can be divided into
source, propagation and receiver. To achieve the aims oceanography and meteorology
theories and methods were required.
5.1
Basic acoustic properties
To generate sound a vibrating source and a medium with mass and elasticity are required.
The vibrating source displaces adjacent particles in the medium. The elastic forces of
the medium brings the particle back to its initial position. The initial displacement
has however forced neighbouring particles to move. The interaction between source and
medium results in a sound wave propagating from the source through the medium with
a frequency determined by the vibrations of the source. Thus, sound is associated with
pressure fluctuations and particle movements [25].
Sound pressure variations and particle motions are related through the impedance of the
medium. Eq. 5.1 shows the relation [5]
p = uZ,
(5.1)
where p is the acoustic pressure, u is the particle velocity and Z is the acoustic impedance
of the medium. The acoustic impedance is dependent on the properties of the medium.
The equation of state for the sound speed is given by the density, salinity and temperature.
The relation is not “obvious” and the sound speed is calculated by using mathematical
scripts. By tradition pressure is commonly expressed in relative form both in air and in
water. The decibel scale is used where pressure is related to a reference pressure. The
underwater sound pressure level is calculated with Eq. 5.2. In this study sound pressure
levels (SPL) are used to express the ambient noise and is calculated as follows
SP L = 20 log10
p
,
pref
(5.2)
where pref is 1 μPa for underwater acoustics [5]. Note that a different reference pressure
is used in air acoustics.
9
In statistics a process such as a time series, is either stationary or non-stationary. The
condition for stationary processes is that the probability distributions do not change with
time. Thus, it does not matter when the signal is recorded; its statistical properties will
not change. For example a linearly increasing signal is not stationary since the mean will
change with time. Sound might adhere to these two kinds of properties.
Stationary signals are divided into two sub-groups, deterministic and random signals.
At every moment in time the value of a deterministic signal can be predicted, while
for random signals only statistical values such as the average is known. Non-stationary
signals are divided into continuous and transient signals. It is difficult to give a definition
of transients. It is often regarded as a short pulse where short is related to physical
phenomena. In contrast, a continuous signal appears during longer time intervals, relative
to physical phenomena. A signal can be regarded as transient or continuous depending on
the situation. The classification of the signal lies in the eyes of the beholder. A common
definition is that transient signals can be dealt with in full, while a continuous signal is
analyzed in sections [20]. In this study the ambient noise of the Baltic Sea is investigated
and the sound signal is regarded as random in character.
In the paper of Wenz [26], the underwater acoustic sound sources where divided in three
categories. In the Bothnian Sea the following sources composes the ambient noise:
• Water motion;
– wind,
– waves,
– bubbles,
– precipitation.
• Man-made (anthropogenic);
– shipping,
– industrial activities.
• Marine life;
– animals.
5.2
Relevant sources of noise in the Baltic Sea
The ambient noise levels depend on wind speed in the frequencies between 200 – 10 000
Hz. Wenz (1962) found that the noise level maximum is in the interval 400 - 800 Hz. The
ambient noise below 200 Hz is independent of wind speed except in shallow areas. The
noise level in shallow water for the same sea state as for deep oceans is about 5 dB higher
[26]. Urick (1983) [22] showed on the other hand that at calm winds the ambient noise
levels in shallow water are often lower than in deep and the opposite relation pertains
at high wind speeds. Poikonen (2010) [17] showed that the wind speed had a strong
influence on ambient noise in shallow seas, especially at lower frequencies. His research
was made within the archipelago, at an isolated place with no ship or industrial noise
influences.
10
Sea States are often used to describe meteorological conditions. Sea states are scaled from
zero to eight and each sea state is defined by wind speed and significant wave height. In
this thesis the sea states defined by the Swedish Defence Material Administration (FMV)
[5] are employed, cf. Table 5.1.
Table 5.1: Definitions of the sea states according to the Swedish Navy [5].
Sea State Wind Speed [m/s] Significant Wave Height [m]
0
0.0-0.2
0.5
0.3-1.5
1
1.6-3.3
0.6
2
3.4-5.4
0.8
3
5.5-7.9
1.2
4
8.0-10.7
1.9
5
10.8-13.8
2.3
5+
13.9-17.1
2.7
6
17.2-20.7
6+
20.8-24.4
7
24.5-28.4
7+
28.5-32.6
Defining sea states based on wind speed is not entirely correct. Water motions generated
by wind may vary. The wind speed alone does not suffice to explain the sea state, also
wind direction and duration has to be taken into account [26]. To keep the research
methods in this study as similar as possible to Wenz (1962) exclusively wind speed was
used to define the sea states.
Sairanen (2014) [21] presented results from measurements made in the Finnish Bay, at
the border of the archipelago. She showed that there is dependence between wind direction and ambient noise levels, see Fig 5.1. She noted as well a clear correlation between
noise and wind speed. Sairanens results are in line with the results presented by Wenz
(1962), Urick (1983) and Poiokonen (2010). Her research was part of BIAS. The data
origins from the same type of sensors as were used in this study.
11
Figure 5.1: Averaged noise levels as a function of wind speed for Jussarö, in the Finnish
Bay, in January in 1/3 octave bands. Collected from Sairanen (2014) [21].
The result of Poikonen (2010) [17] showed that the ambient noise was dependent on wind
speed. He introduced a wind-speed dependent factor. The result was based on measurements in shallow water in the Baltic Sea. The dependence factor was found to be 2.5 for
100 Hz and decreasing to 2 for 500 Hz and higher. These values were higher than those
presented by Wenz (1962).
Even in totally calm weather micro sized bubbles in water add up to bigger and bigger
bubbles that ascends to the surface, oscillating and generating noise [26]. One of the
main sources of natural ambient sound at low frequencies are bubbles created by breaking waves, which in turn are produced by wind. Water droplets are also created from
spray and spin drift. Precipitation, such as hail, sleet or water droplets, generates sound
when penetrating the water surface. A rule of thumb states that precipitation over 2.54
mm/h (1 in/h) raises the ambient noise levels. At sea state 1 and below when breaking
waves are rare, precipitation contributes to the noise levels. For Sea States above 1, no
conclusions have been made due to the complexity of separating wind generated spray
and spindrift from precipitation noise [26]. The measurement of Poikonen (2010) [17]
was made at an inshore place with no influence of ships. His result therefore shows the
meteorological influence on the ambient noise and a strong decrease in the ambient sound
levels below 500 Hz. Further, results were presented on correlation between the ambient
noise curve with the noise spectrum of oscillating bubble clouds created by waves. The
low sound levels of ambient noise below 500 Hz were attributed to the lack of ship and
industrial induced noise.
Ice is known to generate noise in a broad frequency range [26]. Urick (1983) [22] showed
that an ice covered sea could work as a band-pass filter. High and low frequencies are
12
filtered out. Sairanen (2014) [21] noted a decrease in sound pressure levels due to ice in
the Baltic Sea. Unfortunately, during 2014 the Bothnian Sea was never covered with ice
and therefore it was not possible to investigate ice in this study.
In acoustics sound sources can be assumed small if the distance to the source is much
larger than the extension of the source. This was the case in this study where the majority of ships were at a distance 5 km or longer.
Anthropogenic sound can further be categorized as intentionally or unintentionally. Shipping noise is unintentionally sound generation, since the noise from shipping is a byproduct of its activity. Seismic surveys, on the other hand, may be regarded as intentionally generated noise since the sound is used to map out the sediment structure.
Shipping noise is a combination of noise generated by cavitation, turbulence and vibrations from on-board machinery. Propulsion systems are the most dominant part. The
noise generated from ships are classed as low (1-10 Hz), medium (10-500 Hz) and high
(500 Hz - 20 kHz) frequencies [26]. The higher frequency components in shipping noise
are not affecting the ambient noise levels with any significance but due to the high attenuation of high frequency sound in water, it is only affecting the close vicinity of the ship.
It is important to distinguish between nearby and distant shipping. Distant shipping
noise is the noise from ships at a distance where a single ship cannot be attributed to the
sound levels. The opposite prevails for a nearby ship. The sound levels will be dominated
by the ship and the source can be identified. Thus, ship noise dominates at frequencies
between 20-500 Hz but it has an influence on the ambient noise in the interval 10-1000 Hz
[26]. At a well-defined shipping lane this distance will show where the sound levels will
be range dependent. Outside this distance the sound will be determined by the distant
shipping. The results by Sairanen (2014) [21] showed a clear “knee” where ships didn’t
significantly influence the ambient noise, see Fig. 5.2.
13
Figure 5.2: Averaged noise levels as a function of distance to ships during January in the
Finnish Bay in 1/3 octave bands. Collected from Sairanen (2014) [21].
In Fig. 5.2 the “knee” is clearly visible at 4-5 km distance. At lower distances the sound
pressure levels are increasing and at longer distances they are independent on the distance
to the ships.
Ships generate sound below 50 Hz that emanates from the propeller and the hull. The
sound levels have be shown to be dependent on the depth of the two sources, i.e. the
draught of the ship. Due to boundary conditions the sources in the water will create
image sources at the water surface, which will add the noise level up, also known as the
Lloyd-mirror effect [26].
Industrial generated noises such as pile-driving, hammering and other intermittent activities may be regarded as ambient noise and not, depending on the purpose of the
measurements. Offshore wind farm generated noise may however be regarded as ambient
noise since it is always present.
Animals are known to produce sound to communicate, orient and to hunt. The sounds
have many different characters. The cod is for example known to produce grunts, especially when spawning. Other animals such as whales produce a repertoire of sounds, both
short pulses and longer continuous songs. Biological noise varies with time, location and
frequency and is an important part of the ambient noise [26].
14
5.2.1
Sound propagation, refraction and absorption
Sound propagates outwards from a sound source as a spherical wave. Since the Baltic Sea
is shallow the waves will interact with both the surface and the seabed. The propagation
will be altered and the spherical symmetry will be lost. Under certain circumstances
the spreading will become cylindrical. Measurement of attenuation shows that often the
spreading falls between the spherical and cylindrical geometry [5].
Sound waves produce relative motion between water particles. The kinetic energy is
transformed to heat due to friction force. This transformation of energy is called absorption and is especially relevant for high frequencies and long propagation distances [5].
Urick (1983) [22] gave three explanations for the absorption. First, magnesium sulfate in
the water absorbs the kinetic energy of sound. Second, the shear viscosity and third the
volume viscosity contributed to absorption. Urick concluded that absorption increases
with increasing salinity and frequency. He also introduced the absorption depth-factor
that decreases the absorption with 2 percent with every 300 m of depth. The end product,
the absorption coefficient, is approximately 0.02 dB/km at 500 Hz and 1 dB/km at 10
kHz for the Baltic Sea with salinity of 7 PSU, practical salinity unit, and a temperature
of 5 ◦ C [5]. Even at distances of 100 km the absorption is less than the errors introduced
by the methodology. The absorption in shallow seas is rather dependent of the bottom
characteristics. Sea floors such as clay increase the absorption of sound massively [22].
The low salinity, the short propagation distances of the Baltic Sea and the dominating
absorption by the sediment makes the Baltic Sea soundscpae environment complex.
The phenomena investigated in this study are related to frequencies between 10 Hz and
10 kHz, shallow depth and low salinity. The absorption in the Baltic Sea is dominated
by the properties of the sediment; thus, absorption in water can be neglected.
As presented in chapter 5.1, sound speed increases with increasing salinity, pressure and
temperature. The temperature has the largest impact on sound speed. During summer
season the surface layer in the Baltic Sea is heated by the influx of the sun, which results
in a temperature rise at the surface, the so called thermocline is developed, often located
at 15- 20 m depth. At larger depth a halocline (change in salinity) is separating the bottom layer from the other water volume all year around [5]. This results in a high sound
speed at the surface and at the bottom layers and lower sound speed in between the two
layers. Sound waves that propagate between two layers will refract towards the center
of the water column and thus be trapped therein. This phenomenon is named a sound
channel and the most known is the Sound Fixing and Ranging (SOFAR) channel, found
at a depth about 1000 m, in the deep oceans [22]. Sound might travel long distances in
these channels. In a SOFAR channel ship sounds can propagate up to a few 1000 km
[26]. Sound channels acts as a low-pass filters but too low frequencies components of
sounds are cancelled out [22]. The top boundary tends to keep air borne sounds from
entering the channel. Sound channels are well known by both Navies and whales, the
former using it for surveillance and the later for communicating long distances. The
Baltic Sea is somewhat more complex and has a seasonal component that has to be taken
into account. Under certain circumstances sound channels exists, especially during the
summer seasons [5].
15
5.3
Ambient noise
After the second World War the field of underwater theatre started to be systematically
exploited. The Physics of sound in the sea was published the same year as Knudsen et
al. (1945) investigated the noise dependency of the sea states and Wenz (1962) updated
their results almost 20 years later.
The curves of Wenz are spectral presentations based on average sound pressure levels
produced by a number of independent noise sources. Wenz based his research on the
results by Knudsen and he described Knudsens result as Knudsen curves. Today, the
result are known as Wenz curves and is illustrated in Fig. 2.1.
A less used graph presented by Wenz describe average noise levels for deep and shallow
water at no traffic and average traffic situations. This graph shows clearly that shipping
noise is dominant in 20-500 Hz frequency band as presented in chapter 5.1. It is included
in Fig. 2.1. The traffic noise fields (pale blue and blue) are corresponding to these results.
That information is also of importance for navies since the hydro acoustic profile changes
with a war scenario, and it is above all the shipping that changes.
5.3.1
Rule of fives
In the frequency interval of 500 Hz- 5 kHz Wenz (1962) formulated an empirical formula
that described the behaviour of the ambient noise. The rule was formulated based on research done in deep water without any information of wind direction and duration or the
bottom characteristics [26]. The estimate was named rule of fives and has been applied
on others research with good accuracy. The first rule says that between 500-5000 Hz the
ambient sea-noise spectrum levels decrease 5 dB per octave with increasing frequency.
The second rule says that between 500- 5000 Hz the ambient sea- noise spectrum levels
increases 5 dB with each doubling of wind speed in the range 2.5- 40 knots.
5.3.2
Acoustics of the Baltic Sea
The Baltic Sea is located in Northern Europe. Border states are Sweden, Denmark,
Finland, Russia, Poland, Lithuania, Estonia, Latvia and Germany. The Baltic Sea can
be divided into seven sub regions, which are illustrated in Fig. 5.3 [24]. This study is
performed in the Bothian Sea and many Baltic Sea characteristics are the same for the
Bothnian Sea.
16
Figure 5.3: The map illustrates the division of the Baltic Sea in sub regions. It is collected
from HELCOM [9].
The Baltic Sea is a small isolated sea only connected with the North Sea through Baelt
and Öresund. Baelt and Öresund are both narrow sounds which restricts the exchange
of water between the Baltic Sea and the North Sea. Althought the Baltic Sea has a rich
inflow of fresh water from rivers, lakes and precipitation. This in combination with the
low inflow of ocean water makes the salinity of the Baltic Sea low compared to the oceans.
The water in the Baltic Sea is not salt, but brackish. The salinity varies in the Baltic Sea
both with location and time of year. Bothnian Sea is located north in the Baltic Sea and
is much less saline than the southern parts. For comparison, the salinity in Bothnian Sea
is about 5 PSU and 8 PSU at Bornholm Deep at the same time [24].
The Baltic Sea is also shallow compared to the oceans with a maximum depth of about
459 m at Landsortsdjupet. The mean depth is about 54 m, which is less than what Wenz
referred to as shallow. Also the coastline with the many islands and the stratification
patterns of the water makes the sea unique [18].
The bottom characteristic varies through the Baltic Sea. In the Bothnian Sea, pertinent
to this study the seabed consists of strata formed at the quaternary period. Below a 100
m thick layer from Ordovician is found and below that a layer formed at the Cambrian
period. A bit east of the study area the bottom consists mostly of Jotninan sandstone [24].
17
The warming of the Baltic Sea is often rapid and a thermocline is created at a depth of 1520 m. At the autumn, the surface temperature drops and the influence of the thermocline
weakens. Together with the commonly recurring autumn storms the thermocline disappears and the top layer of the Baltic Sea gets well mixed. This is essential for oxygenation
of the water. During winter the thermocline is absent. The mixing can be expected to
be effective down to a depth of 70 m. At 60 - 70 m a halocline is present dividing the
top layers and the saline bottom layer [24]. The halocline and the thermocline make up
the two boundaries that constitute the sound channel in the Baltic Sea. Their presence
will change the wave propagation of low frequency sound and has to be taken into account.
The oceanographically characteristics of the Baltic Sea environment and the fact that the
Baltic Sea is one of the most densely trafficked seas [9], makes the ambient noise situation
unique. The noise levels are expected to differ compared to the large oceans [18].
18
6
Theory Part II: Signal processing
and analysing
Recorded information from physical properties often results in complex signals in the
presence of unknown noise. To understand the signals, conversion into digital form followed by analysis using various algorithms called digital signal processing (DSP) and time
series analysis, is required. It causes a need of careful planning and a conceived strategy.
To realize a complete analysis all prior knowledge of recorded physical and noise properties is vital [1]. In this study the recorded signals and noise are an electrical quantity
delivered by transducers that transforms the acoustical pressure to electrical energy. The
electrical quantity is in a linear relation with the acoustical pressure and therefore sounds
are also referred to as both signals and noise in this thesis.
In real life the recording of a significant amount of acoustic sounds require large data
storage space. The data is often unmanageable to handle and interpret. To extract features that describe the data, signal processing methods were applied. Signal processing
methods were also applied to make quality check of the data. To describe the signal it
has to be described in both time and frequency domain.
6.1
Stationarity
The knowledge of statistical properties of recorded data is fundamental in time series
analysis. An important statistical property is stationarity of the probability distribution.
Stationarity answer the question of how much is the statistical underlying mechanism expected to temporal vary. Stationarity is fulfilled in systems that achieved a steady-state
[19]. Commonly used statistical methods such as correlation and Fourier transform are
only valid if the assumption of stationarity holds true within the estimation window [1].
Thus, it is important to test whether the signal is stationary or not and to what degree.
From a philosophical point of view a signal is either stationary or non-stationary. The
requirement is that the statistical estimate of the stationary process does not change over
time [19]. This can be illustrated with an example: A time series is divided in two data
sets x1 , ..., xn and x1+t , ..., xn+t . If the probability density function of the two sets are
equal, then the sets are strict stationary, if not, they are non-stationary [12].
In an practical perspective stationarity can be classed as strict stationarity, n:th order
stationarity or wide-sense stationarity. Strict stationarity means that the joint probability does not change over time, and neither does the mean or variance. Not all random
19
processes and real recorded data fulfill this requirement but shows stationary behavior.
The weakest form of stationarity is the wide-sense stationarity which is also called weak
stationarity. Wide-sense stationarity means that the mean of the signal (first order statistical moment) is constant and the covariance is only dependent on the time lag (second
order statistical moment), not time itself [12]. A stronger form of stationarity is n:th
order stationarity and means that all statistical moments up to order n are stationary.
In spectral analysis, second order forms a break point between strict and n:th order stationarity, which implies that the stationarity of a signal does not contribute significantly
more to the concept of stationarity with a higher order. Thus, the stationarity of second
order is comparable with strict stationary and higher orders are not further investigated
in this study [19].
Temporal stationarity is a function of time and amount of data samples. However, the
contribution of the sound sources changes both temporally and spatially which affects
the stationarity of the signal. Also the amount of samples affects the validity and the significance of stationarity. Levonen (2005) [11] concluded that a time window of 1.5 s was
appropriate to use in underwater acoustic ambient noise analysis. Choosing an appropriate size of time window is important since if poorly selected, the time series may deviate
to much from the assumed stationarity and the results gets invalid [19]. Levonen (2006)
[12] also presented that the ambient acoustic noise in a shallow bay of the Baltic Sea was
stationary for 0.4 s and with decreasing depth the stationarity decreased. Levonen (2003)
[10] also showed that stationarity of ambient noise may have a dependency on time-of-day.
6.2
Outliers
An outlier is a data observation or value that lies at an abnormal distance from the mean
of other values in a data set. The recorded data in BIAS consists of samples within a
certain amplitude range and normally less than 1% of all values are exceeding this range.
These extreme values are treated here as outliers.
Outliers are known, or strongly suspected, to be due to effects that are not from a physical underwater acoustical measured quantity [4]. One such effect is electronic noise in the
recording system. When dealing with signal processing, measures for determine whether
the signal contain outliers and to what extent is needed. Due to large data sets automatic
processing methods for outlier removal is appropriate.
Great care has to be taken when defining outliers. However, the outliers might be a result
of the experiment and should therefore be included in the data for signal processing. It
could also be ”a result of gross deviation from the prescribed experimental procedure” as
Grubbs (1969) [8] stated it. If the outlier is a bi-product that has nothing to do with the
assumed measured signal the outlier should be removed prior the estimation of underwater acoustical measures [8].
20
Some recorded data may contain many multiple outliers. In such case the cause of the
outliers has to be identified. There is a risk that identified outliers belong to the actual
signal, and if so, the sorting of outliers has to be done manually.
6.3
Correlation
Correlation functions are used in statistics and signal processing to determine relationships between two different sets of measured data. However, some care has to be taken.
Correlation estimates should be based on a physical assumption that is a known or
hypothesized relation. Two time series with no physical relation will often produce a
correlation that could be used to interpret the relation. Two functions of correlation are
mainly used, auto-correlation and cross-correlation. The former measure how well future
values can be predicted using older data. The latter is often used to reveal the similarity
of two signals as a function of the time delay between them. Both signals and noise are
often analyzed with cross-correlation. Auto-correlation may be used to find specific tones
in the noise, which is relevant to the use of sonar systems [1].
The correlation is estimated as the integral of the product of the two signals. Two identical
signals generate a value of one at zero delay, and opposite totally different signals are
un-correlated and generates a value of 0 [1]. Cross correlation is further explained with
an example of two identical signals where one is time delayed with 20 samples displayed
in Fig. 6.1.
Figure 6.1: Example of cross-correlation between two signals of random character. The
red line indicates zero time lag and the blue line indicates the correlation at each time
lag.
21
The location of the peak indicates that the two signals are well correlated at a delay of
20 samples. By changing the order of the signals in the cross-correlation function, the
peak would shift to the negative side of zero on the x-axis [20]. The order is important
due to the causality of many phenomena.
6.4
Spectral analysis
For an extended analysis of noise and signals, estimation of the spectral content is required. Recorded time series are presenting an amplitude, in this case the acoustic pressure, at every sampled time stamp. The frequency domain representation of the data
is independent of time but returns the amplitude and phase for each frequency. Visual
inspection of a data set in time domain tells when different pressure fluctuations appear
while a spectral analysis returns at what frequency it does. The energy for a signal is
conserved i.e. the energy is equal in both time and frequency domain in accordance with
Parseval’s identity [1]. Spectral analysis is a standard method to inspect both the noise
and signal contents in recorded data. For a broader understanding of signals and noise
both temporal and spectral analysis are required. It need to be emphasized that spectral
analysis is only a preliminary data analysis tool. Spectral estimates should not be used
to answer specific questions about data such as whether a sonar pulse is present, but only
suggest possible hypotheses. Detection is a statistical tool and should not be mixed with
spectral analysis.
6.5
Fourier analysis
In spectral analysis the use of the Fourier transform is essential. The analysis is based on
that an arbitrary periodic signal could be written as a sum of sine and cosine functions
to be Fourier series. Jean- Baptiste Fourier formulated this early in the 19th century. His
theories became well used and further developed. Today non-periodic signals may also
be expressed with a sum of sine and cosine elements by using Fourier transform [20].
6.6
Power Spectral Density
A convenient way of presenting signal and/or noise power is to estimate the power as a
function of frequency by use of Power Spectral Density (PSD) displays. The PSD display
may look different depending on type of underlying signals in the recorded time series,
i.e. short spikes are displayed as broadband components. It is established when analysing
and displaying stationary continuous signals to use the PSD as the amplitude squared as
a function of frequency, e.g. V 2 /Hz [20].
22
6.7
Bandwidth
The bandwidth of recorded data is the difference between the uppermost (highest) and
lowest frequency component of a signal, i.e. if a signal consists of frequency components
10 – 50 Hz the signal bandwidth is 40 Hz [25].
Octave bands are common in acoustics. The mid frequency in each octave band is the
doubling of the prior octave band. Historically, sound pressure levels are usually divided
into 1/3 octave bands. The reason behind this is that a 1/3 octave band represents the
critical bandwidth of a human ear. The 1/3 octave bands are defined in Eq. 6.1 where
the mid (centre) frequency fm gives the name to the 1/3 octave band [25].
fu,l = fm 2±(1/6)
(6.1)
The bandwidth off each 1/3 octave band increases with increased frequency. The ratio
between the band frequency and the bandwidth is constant. Consequently, the 1/3 octave
band is suited to display in a logarithmic scale [20].
The ambient noise is in most cases described in 1 Hz bands but in some cases also in 1/3
octave bands. In a technical point of view 1 Hz bands are easier in many applications to
interpret but in sonar applications 1/3 octave band is sometimes handy.
23
7
Theory Part III: Passive sonar
Water is a most effective medium for transport of sound. A ship can be detected long
before it is visually observed at the surface. Individual ships can be heard at 1000 km
distance provided that a sound channel exists [22]. Navies utilized this fact and have been
developing different means to “listen” to underwater sound. The most common sensor
for both listening and transmitting sound is the sonar. This word is well known but few
know that it is an abbreviation for Sound, Navigation and Ranging (SONAR). There are
two types of sonars, passive and active. Passive sonars are dealt with in this study. A
passive sonar is also called listening sonar since it detects sound radiated from the target
(source). Active sonars generate sound-pulses that travels through the sea hitting the
target and returns to the sonar as an echo, cf. radar. Active sonars are used by naval
war ships to locate submarines while submarines use passive sonars to locate other ships
[22].
Active underwater echo ranging was developed before the First World War to detect
icebergs at far distances. At the outbreak of the First World War the interest of sonars
in military application amplified. Both active and passive sonars were developed during
the war. A passive listening device called the Eel, consisted of twelve air tubes mounted
along a neutral buoyant line array towed by ships, was used to locate submarines [22].
Using cross bearings with a group of 2-3 Eels it was possible to obtain a “fix” on a sound
source. Active sonars were employed in the hunt of German submarines but without
success. The breakthrough of active sonars had to wait till the Second World War [22].
After The First World War German papers on underwater acoustics became public and
results were presented on the behaviour of sound propagation due to salinity and temperature gradients. The paper was far ahead in time and was unrecognised for 60 years
[22].
During the Second World War, the United States developed a simple and cheap sonar
system that was mass-produced. The sonar system was placed on-board many surface
ships of the United States and played an important role in the victory of the Atlantic
Battle [22].
The development of advanced sonar systems has been followed by more silent submarines.
The development during the Cold War was no exception. The active sonars became better and cheaper and eventually they found their way to the commercial market. Active
sonars became standard on merchant and fishing ships, both for depth control and fish
location. Today it is also standard system on-board pleasure boats to measure the depth
[22].
24
7.1
Purpose and use of passive sonar
The purpose of using passive sonars is to locate ships without revealing the location of
the sonar carrier. Presently, submarines are equipped with a few different passive sonars
placed at different locations on the hull. Buoys can also be equipped with passive sonars.
These can be dropped into the ocean from ships and aircrafts. Buoys have a limited capacity since the battery charge is limiting the operation time. An expensive alternative
is to place fixed passive sonar systems in the oceans that constantly survey the water
volume. A surface ship towing a long passive sonar array keeps the submarine uncertain
if it is hunted or not. To effectively detect submarines at low frequencies these arrays has
to be many hundred meters long [5].
7.2
Passive sonar equation
In this chapter an introduction to passive sonars is presented. The theory and results
herein are all gathered from open sources. No classified information is presented in this
thesis. A simple passive sonar model (SOFAR) was employed and used in the estimates
of detection ranges.
The sonar equation for passive sonars is a starting point for estimating detection ranges
of a ship. The sonar equation is presented in Eq. 7.1 and consists of five terms which are
presented in Table 7.1.
The sonar equation is based on the assumption that wave propagation is exponential.
Thus, it is possible to relate the different terms as a sum of logarithmic values. This
relation is automatically fulfilled for sound pressure values that are defined as logarithm
of a relative pressure (in dB relative to 1 μPa). The sonar equation is defined as follows
T L = SL − N L − (−DI) − DT
where the variables are defined in Table 7.1.
25
(7.1)
Table 7.1: The parameters of the passive sonar equation and brief explanations of them.
All parameters are measured in dB. The table is a recreation of table 2.1 in Urick (1983)
[22].
Term
Equation
Transmission loss
TL = 10 log10 IIst
Source level
SL = 10 log10 IIrefk
Noise level
N
NL = 10 log10 IIref
Directivity index
DI = 10 log10
PNekv
PNS
Detection threshold DT = 10 log10 PPNR
0
Explanations
Is = Signal intensity at 1 m
It = Signal intensity at target
Ik = Source intensity at 1 m
Iref = Reference signal intensity
IN = Noise intensity*
Iref = Reference signal intensity
PNekv = Power generated by ndh*
PNS = Actual Power generated
PR = Signal power needed
PN0 = Noise power
* Non-directional hydrophone.
TL is the difference of the source intensity and the intensity at a range r. It depends on
geometrical spreading of sound, anomalies in water and the current absorption.
SL is the intensity level 1 m from the source measured in 1 Hz band compared to the
reference intensity. The reference intensity is calculated for a signal consisting of a plane
wave with rms 1 μPa.
NL is the unwished surrounding noise level. In this study NL is the ambient noise measured in 1 Hz -bands. It changes with sea states.
To reduce the influence of noise, multiple hydrophones can be employed mounted in an
array configuration. By keeping the main axes of the array orthogonal to the target direction the ambient noise is reduced relative to the source level. The source to noise ratio
is improved and the source can be detected at longer ranges [5]. With an array length of
25 m, 5 dB directivity gain (DI) can be achieved at a frequency of 100 Hz.
Detection Threshold (DT) is the signal to noise ratio needed to detect a target with a
certain confidence. It is set by the operator. With a decrease of DT an increase of false
alarms will follow and with increased value of DT an increased probability to miss the
target is followed [5]. It is thus a trade off. Experience from operations shows that a DT
of about 9 dB is a good choice. In this study broadband detection was employed since
no specific tone was assumed for the target. If the target is producing a specific tone
that is known by the operator, it is optimal to apply a sharp filter that detects changes
in frequency amplitude (narrow band detection) [5].
Both noise level (NL) and transmission loss (TL) are dependent on weather, location and
hydrography, which in turn are dependent on time of year. The environmental parameters have to be deduced by in situ measurements or calculated for each position and
situation. The transmission loss is even more difficult to determine than the ambient
26
noise since it depends on spreading, sediments, anomalies and the hydrography. For example if the hydrophone is placed within a sound channel and the target is outside the
TL will be higher than if the target also was located in the channel. To investigate the
local transmission loss a numerical estimate was calculated using a LOFAR sonar. The
frequency of the source was 100 Hz and the water depth was 70 m. The sonar was located
at 63 m depth. The sound channel was present in the middle of the water column. The
results are shown in Fig. 7.1. The sound is trapped in the middle of the water column.
Figure 7.1: Transmission loss in the Bothnian Sea in January. The illustration indicates
the transmission loss for differrent source placement in the xy plane. The colour-bar
indicates the values of each colour. The values are in dB re 1 μPa. Result computed with
software SonaCalc.
This result visualizes the behaviour of sound in water in the Bothnian Sea. According
to this result it is most favourable for a submarine to stay in the pale blue areas, since
that is where the transmission loss is the greatest. In this case that would be almost
at the surface and at the bottom. a depth of 34 m would should be avoided since the
transmission loss is less strong at that level to a distance of about 10 km. These results
are important for the submarine operator.
27
8
Method
The aim of this study was to establish the sound levels of the ambient noise and investigate
the detection ranges for a basic sonar surveillance system. To achieve these aims a number
of data sets were used. The recorded sound data as well as wind, wave, hydrography and
AIS data were used. For the estimation of detection ranges the sonar equation was used
where background levels were taken from the own produced results.
8.1
Data collection
The sound data used in this study was measured 2014 by the BIAS project. Meteorological and ocean data were produced by SMHI and pre-processed by AquaBiota Water
Research. The AIS data was supplied by HELCOM.
8.1.1
Noise recordings
The hydrophone used was of the type SM2M logger from Wildlife Acoustics and was
placed at N 61.75738◦ , E 19.31642◦ . It was anchored at the sea bottom at 63 m depth.
The deployment position was chosen to be outside the shipping lane. The sampling frequency was 32 kHz. The rig was deployed in November 2013. The recording started
at the 1st of January and ended 31st of December. The recording time was limited to
three months where after the memory was full the sensor had to be replaced. This procedure was repeated throughout the 2014. The recording length was 23 minutes every
hour every day for a year. The main component of the rig was the autonomous recorder
that contained a hydrophone, amplifier, filter unit, A/D converter and a storing unit. A
sketch of the rig is showed in Fig. 8.1 [23].
28
Figure 8.1: Sketch of the BIAS standard rigs. The rig to the left uses the Loggerhead
sensor and the rig to the right the Wild Life Acoustics. 1 hydrophone, 2 extra buoyancy,
3 & 7 autonomous loggers, 4 acoustic releasers, 5 anchors, 6 buoys [23].
Calibration of the system was completed both before the first and after the last deployment. The aim of the calibration was twofold; first to control the quality of raw data and
second, more important, to establish the sensor sensitivity, that is the relation between
the pressure variations and the recorded data. The calibration gave the sensitivity in
bins/μPa. The reason for this “odd” entity is that the recorded sound was stored in a
wav-file, which scales data in 216 bins. To convert the bins to pressure the scaling factor
was needed [23].
The hydrophone is connected to two separate channels. Thus, here was the option to
amplify one of the internal amplifiers of the autonomous sensor. This has to be done
with care. A too high gain results in clipping when strong sound sources pass, which will
have an uncorrectable effect on the sound average estimate. On the contrary, if a too low
gain is set, the signal-to-noise ratio might be too low [23]. The first channel was set to
zero amplification and the second to 12 dB.
29
8.1.2
Meteorological data
The meteorological data was pre-processed by AquaBiota Water Research but was originally produced by SMHI. The data used was significant wave height, ice thickness, salinity
and temperature of the water for every sixth hour as well as wind speed data for every
hour. The meteorological data were model based estimates for a position close to the
actual hydrophone position.
8.1.3
AIS data
AIS, Automatic Identification System, is a world-wide system that makes it possible to
identify and track ships from other units or land-based stations. The purpose of the system is to increase the safety at sea. The Swedish Maritime Administration is responsible
for AIS data distributed within Sweden. HELCOM is the data holder for all AIS data
to be used in BIAS. The BIAS project has allowance to use the data for 2014. The AIS
data was stored in a text-file format containing date, time, speed, position, dimensions
of ship, draught, type of ship and cargo.
8.2
Signal processing
There is a number of processing steps that can be applied to a time series. The different
options at hand will affect the estimated properties. How to choose and implement processing methods lies in the hands of the processor. Every set of data can be measured,
processed and analysed in multiple ways. There are no rules to adhere to. The methods
employed are often based on experience from earlier studies.
8.2.1
Pre-processing
Pre-processing was performed to prepare data before estimating the statistical properties. It was also done to make sure there were no artefacts affecting the estimates. The
purpose of the recordings was to measure the ambient noise. Some signal content should
not be regarded as ambient noise signals [16]. Electronic spikes are an example of that
and should be removed before starting to estimate the properties of the time series. A
second example is the ambient noise recorded during deployment of the rig, even if it can
be regarded as ambient noise it was not in this study, and it was removed.
30
The processing started with grouping the data into monthly periods. In the first preprocessing test the number of files was counted to make sure no recordings were missing.
In the second test the length of each file was controlled, to make sure that the sensor
had been working properly. Files with substantial difference in length were removed from
the data set. Each noise recording was also controlled for non-numerical values such as
NaNs (not-a-number) and infs (too high value for a numerical representation). All nonnumerical values were excluded from the set [16]. If the sound was too loud the recorded
data became clipped when the amplitude of the signal was higher than the maximum
allowed value of the Analog-to-Digital converter. Clipping was checked for both positive
and negative values.
8.2.2
Grubbs’ test
Self-noise is an unwanted product of the instrument and has to be dealt with. The first
step in this process was to optically inspect the time series for anomalies. When anomalies were found they were inspected both by plotting them and by listening to the sound.
The different types of anomalies were identified and an algorithm was designed that automatically identified and removed the anomalies. A commonly occurring anomaly were
spikes. These were small groups of outliers much stronger than the surrounding signal.
The algorithm that was developed to remove spikes was based on Grubbs’ test [8]. This
is a test which results in identification of outliers in a time series. The significance level
of 5% was used. The algorithm was built up in seven consecutive steps:
1. The data set was divided in windows of maximum 1000 samples.
2. The data within each window was sorted in ascending order; x1 , x2 , x3 , . . . , xn .
3. The ratio of w/s was calculated, where
s = std(x1 , x2 , x3 , . . . , xn .) and
w = xn − x1 .
4. The value of w/s was compared with the value 7.33 collected from table 3 in Pearson
(1964) [15]. If it didn’t exceed, the process started over with a new time window in
step 1, if it did, the process continued with step 5.
5. The value T1 was compared to pV, where T1 was defined according to Eq. 8.1 and
pV according to Eq. 8.2. T1 was calculated as
T1 =
±[xmean − xn ]
s
(8.1)
if the value exceeded pV, calculated as
n−1
pV = Nstd √
n
31
s
t2
,
(n − 2 + t2 )
(8.2)
it was classed as an outlier and the second largest value, xn−1 , in the set was tested.
If not, the process restarted with the next time window in step 1. pV was calculated
according to Dan (2013) [4], where n is the number of samples (in this case 1000),
t is a constant value set to 1.645 and Nstd is the number of standard deviations
[4]. A high value of Nstd results in high certainty that all outliers identified were
outliers but with a risk that some outliers were not found. A low value results in
data identified as outliers even if they were not. In this case Nstd was set to 5.
6. All xi corresponding to a too high T1 were classed as spikes and were removed from
the data set and replaced, i.e. a piece of the data set without spikes was cut-out
and used to replace spikes. Many different methods can be applied to replace data.
After a few tests this method was selected due to good stationarity result.
7. The procedure was repeated with the next time window.
8.2.3
Kolmogorov-Smirnov two sample test of stationarity
In signal processing strict stationary signals are rare. The Kologorov- Smirnow two sample test, also known as the KS-test, are often used to test whether a signal is stationary
or not [11].
The test is based on distribution functions of the two sets. The cumulative distribution
function (CDF) is calculated for each set. They are compared and if there is a nonsignificant difference between the CDFs, the sets are said to be strict stationary with a
certain confidence. This was calculated with Eq. 8.3 [3],
TKS = sup|CDF1 − CDF2 |,
(8.3)
where 1 and 2 denotes the different sets tested. Then a null- hypothesis as
H0 = ”the two sets are stationary”
was stated. H0 should be rejected with the significance level α, if TKS was greater than
(1-α), [3] [12]. The level of significance may be decided by the user and is often set to a
maximum level of 5 %, which was used in this study. A 1.5 second window was used.
8.2.4
Averaging
Noise was recorded with a sampling frequency of 32 kHz. The file corresponded to 23
minutes recording. The file size was 175 MB and it was found to be difficult to handle
more than a few files simultaneously. To reduce the computational time the data was
decimated by calculating averages over a pre-specified time length. Two different methods were used for averaging; twenty seconds means (20 s means) and PSD averaging.
32
A time length of 20 s reduced the data size with a factor of 640000. The length was
chosen for two reasons, first these data can be published without breaching security as
specified by the Baltic Sea Navies, and secondly this time length will have a small effect
on ship passage.
An alternative is to calculate the median. The advantage is that median is not affected
by spikes at the same levels as the mean. It has also been shown that median will filter
out nearby events, provided that they are short, and provide an estimate on the ambient
noise, especially the sound produced by wind and waves. The problem with median is
that it requires more space and power of the computer than calculating the mean. Spikes
will influence the mean especially for sound that are spanning over a large scale but by
using Grubbs’ test and removing identified spikes improved this weakness of the mean.
To verify the validity of the 20 s means, comparisons were made with 1 s means and
median for January 2014 in Bothnian Sea.
Time averages were made over the frequency band 10 Hz – 10 kHz and for 20 seconds
which is useful for many time series based analysis. The information of the frequency content disappears though, and for some analysis frequency information is required. Therefore the power spectral density, PSD, was calculated for each second in every recording
each whole hour. These PSDs were then averaged over each frequency resulting in an
averaged PSD for each hour. By doing this, the frequency information was conserved and
the amount of data reduced.
8.3
Handling of different data sets
As was pointed out by Wenz the ambient sound is generated by a number of sources.
Fortunately many of them are found in different frequency ranges. By relating meteorological, oceanographical and ship data to noise data it was possible to estimate their
influence and hence identify the source strength.
8.3.1
Combining ambient noise and meteorological data
Poikonen (2012) [18] showed that the major parameter is the wave height. Large waves
will brake and produce bubbles that produces sound. Thus, higher waves will induce
more sound. The waves are in turn dependent on wind. As a first approximation it can
be assumed that strong wind will produce large waves. Other published results in this
field also shows that wind place a role in generating sound. Traditionally at sea, wind is
grouped in sea states, based on experiences made in the open oceans. The meteorological
data were produced by SMHI and based on the HIRLAM model. The wave data was
calculated based on the wind data. Clearly the different sets of data were connected.
To analyse wind dependency on the ambient noise, both time series and frequency analysis methods were applied. For time series analysis five minutes represented by fifteen 20
s means for every hour were averaged and combined with the meteorological data. The
frequency analysis was performed by combining the meteorological data for every hour
33
together with a five minutes PSD average for corresponding hour. The wind data was
only given as a single value per hour and wave data even less often, the weather may
deviate within a 23 minutes time frame and therefore was only a five minutes mean used.
8.3.2
Combining ambient noise and shipping data
Since the advent of AIS it has been possible to do statistical analysis of ship traffic. In
this study only ships in the range of 20 km were included. Ships passing outside the
range are assumed not to contribute to the noise [21].
Ships continuously transmits AIS data. The time stamps of each ship transmission were
grouped together and sorted in ascending order. The data showed that the AIS data
sample varied from every 30th second to every 2nd minute and in some cases even less
often. The sorted data was also used to increase the number of positions by employing
interpolation. This scheme resulted in a position series that were synchronized with the
20 s means sound data. 20 s means for 1/3 octave band were also calculated for analysing
the influence of shipping noise at different frequencies.
Results from the work by Sairanen (2014) [21] indicate that ships within 5 km radius are
regarded as nearby shipping. It should be noted that ships have different source levels.
A ship at 9 km may produce the same sound pressure levels as a ship at 4.5 km distance
provided that the source level is 6 dB higher. An investigation of the most common
distance to the hydrophone for the passing ships in January showed a distance over 15
km. To minimize the risk of classing a distant ship as nearby and without any risk of
classing too many ships as nearby all ships within 10 km radius were regarded in the
calculations as nearby.
Analysing ship induced noise is complicated and to make sure noise levels and AIS data
was combined in a correct way for analyses a few steps were performed:
1. Every ship passage was combined with a 20 s mean value of noise level.
2. If two or more ships were passing the hydrophone at the same time, they were all
combined with the same noise level. If one ship passed within 10 km radius to the
hydrophone and one at 20 km distance both of them got combined with the same
noise level. All distant ships passed the hydrophone simultaneously as a nearby
ship, were therefore removed from data.
3. If two or more ships passed the hydrophone within the nearby shipping border (10
km) all data was removed for that time period, both nearby and distant shipping.
4. All other combined noise levels and distances to ships were saved together with
other ship relevant specific. These results were then saved in a table. A piece of
the table is presented in Table 8.1.
34
Table 8.1: Example of the saved AIS interpolated data.
Distance [km] SPL [dB re 1 μPa] Time [seconds] MMSI
5
124
280
1115748
5.5
116
300
1115748
6
112
320
1115748
This table was then used in analyses.
8.4
Method of determining ambient noise and its
dependencies
The method of determining the different noise dependencies was initially performed for
meteorological events and later for human activity, in this case shipping. The methods
are described with a starting point in recording methodology and ending with the final
analysis of the recorded data.
8.4.1
Transformation from time to frequency plane
The raw data was divided into one second time segments, which were transformed to the
frequency domain by using the Discrete Fourier Transform, DFT. An advantage with
using 1-second time segment is that the bandwidth of the PSD, is 1 Hz. The PSD and
the amplitude will thus be identical [16]. There are many different methods to use when
analysing noise and generating PSDs. There are no clear rules to adhere here and after
a few tests with different methods, periodogram was selected since it is trustful when
analysing huge sets of noise. The PSD had in the end the unit (dB re 1 μPa)/ Hz. Tests
were done with and without using tapering windows (Hanning and flat-top). The improvement was found to be negligible, thus, all spectra were calculated without using any
tapering.
8.4.2
Correlation of wind, waves and ambient noise
Correlation is a powerful tool in research, since it can be used to find relationships between different sets of data that not obvious is related. Care has to be taken though, since
correlation may be found between any sets of data due to coincidence and not that they
are related. To make sure correlation can be used, a physical relationship between two
events should be supported by a hypothesis or fact of a physical relation. In this study
relationship between meteorological events and noise is well founded in earlier presented
papers. Three sets of data were analysed with cross correlation:
• The noise dependent of the significant wave height.
• The noise dependent of the wind speed.
35
• The wave height dependent of the wind speed.
Correlation calculations were performed with 20 s means since the frequency content was
irrelevant in this case. A five minute period, i.e. fifteen 20 s means were averaged and
used in the correlation calculation.
8.4.3
Wenz curves based on wind speed
One of the goals in this study was to generate Wenz curves. This was performed in 5 steps:
1. PSDs were calculated in the band 10 Hz – 10 kHz for every noise recording in
January, February, March, May and June. Each hour contained 82800 PSD (1 PSD
per second). These were averaged to one PSD/h. Since sea states are determined
by wind speed ranges it is assumed that the sea state is constant for 23 minutes.
2. January- March PSDs were combined together as winter and May- June PSDs combined together as summer. April consisted of both summer and winter hydrographic
characteristics and not included in any. July data were biased and not used.
3. The PSD average of each hour was attributed to the actual sea state of that hour.
4. All PSDs in every sea state were averaged over each Hz.
5. The averaged PSD for each sea state was spline fitted in a logarithmic scale.
These PSD-curves as a function of sea-state constitutes the Wenz diagram. Sea states
only based on wind speed may be misguiding. A five minutes interval containing fifteen
20 s means each whole hour were averaged and combined with corresponding wind speed
(m/s). By doing that it became possible to compare noise levels at different moments
for the same wind speeds, thereby could the influence of wind direction and duration be
investigated. Using clean wind direction and duration data was not possible.
8.4.4
Ambient noise dependency of significant wave height
The procedure of determining the ambient noise dependency of significant wave height
was the same as for the wind speed except that the sea states were governed by the
wave height. The wave height is however estimated by SMHI using the wind data. It
cannot be expected to vastly improve the estimate of the noise dependence on sea state.
It is expected, at least in the large oceans, that wind direction will have an effect on the
sound levels. A persistent wind will build up a sea that will propagate long distances and
eventually brake and produce sound. It is also possibly to assume a sea breeze would
build up the sea close to shore more effective than a land breeze. This aspect was not
studied.
36
8.4.5
Ambient noise dependency of hydrography
The sound propagation is dependent on the hydrography. Under special conditions a
sound channel develops that will give rise to long propagation distances. In the Baltic
Sea it is the temperature and the salinity that alters the sound speed profile. Studying
the sound speed, based on temperature and salinity as a function of depth, reveals that
there are three distinct periods that has to be studied separately:
• Winter season, when only a halocline is present keeping the more saline and warmer
water at the bottom. The rest of the water column has a low and constant temperature. Sometimes at some places a weak thermocline is built up at the top with
even colder water.
• Summer season, when a strong thermocline is developed at 15- 20 m and there are
only small variations in sound speed between mid-layer and bottom layer.
• Mixed season, when storms mix the water around and no clear clines can be identified. The mixed season were not investigated within this study.
The hydrographic data given were salinity and temperature at different depths and times.
To calculate the actual sound velocity the MATLAB tool-kit SEAWATER [13] was used.
8.5
Sonar range calculations
The sonar equation as presented in chapter 7.2 is the most simple variant of sonar equation. It can be adjust to be used for active sonars by adding a few terms. The sonar
equation can also be adjusted for different types of passive sonars by changing the parameters, e.g. DI is most dependent on sonar type.
The calculations with the sonar equation were performed with the software tool SonaCalc,
written by FOI. The strength of using SonaCalc is that it automatically calculates the
transmission loss due to the actual hydrographic characteristics and the effects of changing
the vertical placement of both the source and the target. A change of sonar depth can
result in significant change of transmission loss due to the hydrographic character, i.e. a
sound channel. The depth of the sonar was set to 63 m and the source was set at the
surface. The source levels were set according to Miasnikov (1995) [14] and Urick (1983)
[22], and is presented in Table 8.2.
37
Table 8.2: Source levels at different frequencies based on papers published by Urick (1983)
and Miasnikov (1995).
Type of ship
Mode
SL at 100 Hz SL at 1000 Hz
dB re 1 μPa dB re 1 μPa
Submarine
Ultra quiet
Periscope depth
Submarine
Quiet
Periscope depth
Corvette
Cruising
Propeller propulsion
100
80
120
100
157
136
By reading the generated Wenz curves at 100 and 1000 Hz for each sea state the different noise levels was determined for the sonar equation. The hydrographic characteristics
together with the noise levels made the sonar equation unique for the actual position.
Further some assumptions of the sonar equipment were made. To keep the sonar type
as simple as possible, the sonar type LOFAR, Low Frequency Analysis Recorder, was
selected. A sonar is ineffective as a single hydrophone and therefore a 25 m long array
was added which resulted in a directivity gain of 5 dB.
The detection threshold was set to 9 dB, which is used as a reference level in several
studies and further corresponds to the detection threshold an experienced sonar operator
would use.
With SonaCalc the calculated transmission loss for every range is compared to the sum
of the noise level, source level, detection threshold, the directivity index and the range
where they are equal gives the maximum range of detection. It is explained with Eq. 7.1.
Other frequencies would in some cases be more optimal to use, but due to lack of open
sources only 100 and 1000 Hz source levels were used.
38
9
Results and discussion
This chapter presents the ambient noise dependency of the weather and ship induced
noise and discusses the validity of the results. Presenting signal processing results and
the interpretation of those and introducing the location meteorological character for the
position.
9.1
Signal processing results
A quick scan of data showed that it contained spikes. It was decided to evaluate the effect
of their presence and to remove them if necessary. With a Grubbs’ test based algorithm,
the ”spike remover”, spikes were identified and removed. One noise recording is presented
twice in Fig. 9.1. First the original data, then the processed data with a transparency is
plotted.
Figure 9.1: An arbitrary selected noise file. The original recorded data is plotted with
blue and the data post ”spike remover” with orange. Both signals are displaced 0.04 in
different directions for clarity.
39
A look at the signal as well as the stationarity test shows that the two versions of the
signal are ”behaving” almost identically. In the example presented, spikes at about 20,
470, 1150 and 1200 seconds (coloured blue) are not present in the orange version of the
signal, which demonstrates that the algorithm effectively identifies and removes spikes.
Most of the spikes were removed, although not all. The reason of this might be borders
set in Grubbs’ test, i.e. some spikes were smaller than 5 standard deviations and not
identified. The energy content in a non-removed spike was investigated and it contained
a low amount of energy and its influence of the mean of the signal is negligible. Those
spikes are only a few samples long and didn’t affect the 20 s means with any significance.
Therefore the spike remover is regarded trustworthy and was used routinely in this study.
By listening to the spikes, it could be concluded that the spikes were generated by a motion inside the hydrophone pounding, or electrical noise, most likely self-noise. It could
also occur due to a fish or bubbles hitting the hydrophone, but without doubt it was not
a part of the ambient noise and was therefore removed from data.
The stationarity test of the signal was done both prior and post the use of the ”spike
remover”. The results from these two tests are presented in Fig. 9.2. The signal was
tested with the Kolmogorov-Smirnov two sample test (KS-test) and each value on the
x-axis represents two time windows. The values on the y-axis, α, are the probability
of an error I or II, i.e. the probability of rejecting a true null hypothesis or accepting
a false. The red line is representing a 5% level which often is used and the green a 1% level.
Figure 9.2: KS-test for recordings in January in Bothnian Sea pre (blue) and post (orange)
the ”spike remover”. The y-axis shows the probability of non-stationarity. The red line
representes a 5% level of significance and the green 1%.
40
The KS-test show that the signal is never strict stationary at a significant level of 99%,
alternatively there is never less than 1% probability of an Error I or II. Lots of time
windows are strict stationary of a significance level of 95%, both before and after the use
of ”spike remover” algorithm. It should be underlined that the level of stationarity of
the signal was improved after the use of the spike remover. Not all time windows were
improved. A hypothesis is that segments with ship passings close to the hydrophone
resulted in a non-stationary signal. This can potentially be used to identify ship passages.This has to be tested since the KS-test also identifies other events with non-ship
origin.
The results of the stationarity indicates that the majority of the signals processed are
strict stationary of a significant level of 95% which implies that the statistical estimates
can be applied. To be rigorous all time-windows with an α> 0.05 should be removed
from the data set. This would have resulted in a totally strict stationary set of data at a
significant level of 95%. This was not done due to time restriction of the study.
The verification of the averaging methods were performed in two steps, and the first is
presented in Fig. 9.3. The figure illustrates a time series of one noise recording á 23
minutes. The figure consists of the original data, the 1 s means and the 20 s means. To
compare them the average of the 1 s means and 20 s means is presented together with
the median of the 1 s means.
Figure 9.3: One arbitrary selected noise recording. Blue illustrates the captured noise.
Red is the 1 s means. Black the 20 s means and the horizontal lines are the mean and
median of the 1 s means and the 20 s means. The original data amplitude is in volt and
the data is amplified and displaced to make the comparisons of the averages and original
data easier.
41
The 1 s means (red) and the 20 s means (black) follow each other well and they are
both following the rms value of the original data, which can be seen as an increase of
the averaged values when the original data field gets thicker. The variance is naturally
higher for the 1 s means but it consists of random amplitude drops. The reason behind
those drops are unknown. The 20 s means doesn’t show such behaviour. Comparison of
the averaging methods and median shows no significant difference between the methods.
The values differ on the second decimal. The conclusions is that the 20 s mean average
method is reliable as estimate and can be used for calculating other statistical estimates.
To test that this also holds for a longer time interval than one noise recording, all noise
recordings of January were combined and tested and the result is illustrated together
with the standard deviation in Fig. 9.4.
Figure 9.4: The mean and median of all 1 s means and mean of 20 s means in January
with the standard deviation.
The median of the 1 s means are slightly higher than the two averages. The standard
deviation is about 5 dB which states that the results are trustworthy. The result verifies
the assumption of reliability of using 20 s means. The main reason for using 20 s means
is the clear reduction of data storage and computational time that may be achieved. A
quick test of data handling showed that it was possible to load three original data files
simultaneously in MATLAB. If more than three files were handled the computional time
increased significantly. Three files corresponds to three hours of data, thus a clear reduction of data storage and computational time is needed and will be achieved by decimating
the data.
The 20 s means were used in an initial step to investigate the time series of the noise
levels. The noise levels in January are presented as a time series in Fig. 9.5.
42
Figure 9.5: Representation of January with 20 s means of the frequency band
10 Hz-10 kHz. Each hour is represented by 23 minutes.
From the time series it is possible to detect changes in the noise at specific time. It is
possible to see natural variations but also anthropogenic generated noise such as the high
and steep peaks. Between 200-300 hours there are two distinct elevations of the noise
level. These may be due to weather while the transients most probably are ships passing
close to the hydrophone.
9.2
Meteorological conditions at the measuring location
To replicate the study it is of importance to know the experimental set up which in this
case mean the natural environment. There are many environmental parameters but in
this study the wind speed, significant wave height and hydrographic character of the location of the noise recordings are described. In this section these factors are presented.
The illustrations only shows a part of the year. The results of the other months that were
investigated are presented in Appendix B.
43
The results in the previous section indicate that the signal consists of ships and weather
induced noise. Wenz (1962) showed that the underwater noise is to a large extent effected by meteorological conditions. A starting point is to divide the time into segments
according to the sea-sate that prevailed. The sea states at the location of the hydrophone
for January are presented in Fig. 9.6.
Figure 9.6: Sea states occurring at the hydrophone location in January. Sea state 0.5
occurred 9 times, sea state 4 once and therefore not plotted. The red line indicates the
mean noise level for all Sea States and the green line the median. The noise levels are
for the frequency band 10 Hz-10 kHz. On the x-axis the noise levels are presented and on
the y-axis the relative frequency of each noise level is presented.
The result shows that the weather in January was dominated by three sea states; 1 (1.63.3 m/s),2 (3.4-5.4 m/s) and 3 (5.5-7.9 m/s). During nine hours the wind speed was in
the sea state 0.5 (0.3-1.5 m/s) interval and only one hour the wind speed was in sea state
4 interval (8.0-10.7 m/s). Therefore sea states 0.5 and 4 were not plotted in the histogram.
The histogram indicates that during January sound pressure levels are correlated with
sea state. The larger sea state the higher was the mean sound level. The mean noise level
for sea state 1 was about 107 dB while for sea state 3 about 113 dB. A distinct difference
between sea states are showed but also variations within the sea states. Sea state 1 varies
between 94 dB and 122 dB. Although the 122 dB noise level is probably due to a ship
passage. Looking at the center of the sea state 1 histogram, there is a variation of noise
levels which indicates that only defining sea states based on wind speed is not sufficient.
A further analysis of this is made in chapter 9.3. The purpose of this presented histogram
was to visualize the meteorological situation of the location.
44
Obviously, taking only meteorology into account is not enough for declaring the natural
environmental variations. A possible factor that can influence the sound wave propagation is the local hydrographical properties. The hydrography for the Bothnian Sea at the
sensor location is presented in Fig. 9.7. The figure illustrates the mean velocity, mean
temperature and mean salinity for the three hydrographic types; winter, summer and
mixed.
Figure 9.7: The hydrography at the location of the hydrophone. Winter (red) is represented
of data from January, mixed (blue) from April and summer (red) from August.
The temperature and velocity profiles has similar shapes, which means that sound velocity depends strongly on temperature. Salinity is also affecting the velocity but to a less
degree.
In winter a halocline is found at 40-60 m which has two effects. It keeps the more saline
water down which increases the velocity in the bottom layer but also keeping the temperature at 3-4 ◦ C which also affects the velocity. Below the halocline the sound velocity
is higher and sound waves will refract up.
A strong thermocline is located at a depth of 10-15 m during summer. The sound wave
moves faster above the thermocline and refracts down. The big difference in temperature
results in a strong down bending refraction of sound.
During spring and autumn a mixed hydrographic character is present. Between 40 and
60 m the velocity decreases slightly, which indicates a weak sound channel. During winter
and summer no sound channels are present.
45
The sound velocity profile has a large impact on the wave propagation. It is a vital
property that submarine and sonar operators must manage to control. The hydrography
is also an important parameter when studying the effect of sound on mammals and fishes.
9.3
Ambient noise in different meteorological conditions
To make sure the assumption of noise level increases with sea state is correct the crosscorrelation between wind speed and noise levels and the cross-correlation between noise
levels and significant wave height were calculated. The assumption is showed to be valid
and the noise levels is correlated to both the wind speed and the significant wave height
which can bee seen in Fig. 9.8.
Figure 9.8: The vertical red line indicates 0 time lag, the orange upper line is the correlation between noise levels and wind speed and the blue lower line noise level and significant
wave height.
The significant wave height data is based on the wind speed data. It can be assumed
that the significant wave height will correlate with the wind speed. It was also calculated
and it correlated at about 2 h time with a value of 0.94. The cross correlation presented
in Fig. 9.8 shows a correlation between noise and wind speed with a normalized value
over 0.7.
The waves also correlates well to the noise data. The different shapes of wind speed and
wave height correlation curves depend on the data sampling. The wave data was calcu46
lated with 6 hours time interval while wind speed data was calculated hourly. There is
no gain in using the wave height, derived from wind speed, the analysis is thus performed
primarily on wind speed data.
Another way of determining that there is a correlation between wind speed and noise levels is presented with Fig. 9.9. The figure shows the wind speed plotted with the same time
axis as the 20 s mean noise levels. This verifies the result of a clear correlation between
noise and wind and also illustrates the variation of noise levels within the same sea states.
Figure 9.9: The noise levels plotted together with the wind speed. Blue line indicates the
noise levels and the red one the wind speed. The rings indicates sea states. The data is
for January in Bothnian Sea.
It is shown that the noise is dependent on the wind speed. It is also clear that the ambient
noise not entirely depend on the wind speed. Some of the noise level variations seems
not to be due to wind speed. This type of analyses gives an insight in the influence of
wind on the ambient noise level.
An alternative to investigate the relationship between noise levels and wind speed is to
plot them against each other, see Fig. 9.10. The result is based on broadband 20 s
noise averages in the frequency interval 10 Hz- 10 kHz. This methodology was used by
Sairanen (2014) for noise recordings obtained at the Finnish coast for January and the
results obtained for the Bothnian Sea confirm her results.
47
Figure 9.10: 20 s mean noise levels in the band 10 Hz-10 kHz for different wind speeds
during January at the Bothnian Sea.
The results show that for a specific wind speed there is an inter-variation of noise level,
which possibly has to do with wind direction and duration. At a wind speed of 3 m/s
the noise levels varies with about 8 dB. This shows that a mapping using wind speed
as sole parameter does not suffice. An interesting aspect is that the variance in noise
levels seems to decrease with increased wind speed. A possible explanation is that strong
winds will locally generate loud noise levels while noise at weak winds might be due to
swells that were produced earlier in some other part of the Baltic Sea. This might be
dependent on location and the presence of islands. The direction of the wind might play
a role. The location of the sensor was in the middle of the Bothnian Sea and the influence
from coastline should be weak.
From Fig. 9.10 it is also possible to verify the results with the rule of fives, introduced by
Wenz. At 3.6 m/s (7 knots) the mean noise level is about 108 dB re 1 μPa. A doubling of
the wind speed should result in an increase of noise level with 5 dB. At 7.2 m/s the mean
noise level is about 113.7 dB re 1 μPa. This is an increase of 5.7 dB, 0.7 dB higher than
expected. The reasons may be that the measurement was done in a shallow sea, the result
was obtained during January when winter conditions were prevailing. Nevertheless, the
result corroborate with those Wenz presented 1962.
The final result of wind dependency of ambient noise is the Wenz curves for the particular
position. This is presented in Fig. 9.11 and Fig. 9.12.
48
Figure 9.11: Recreation of Wenz curves for Bothnian Sea in winter. The graph illustrates
the ambient noise spectra estimated in 1 Hz bands averaged for January, February and
March for different sea states. Blue graph is for sea state 0.5, orange is for sea state 1,
yellow is for sea state 2, purple is for sea state 3 and green is for sea state 4.
49
Figure 9.12: Recreation of Wenz curves for Bothnian Sea in winter. The graph illustrates
the ambient noise spectra estimated in 1 Hz bands averaged for May and June for different
sea states. Blue graph is for sea state 0.5, orange is for sea state 1, yellow is for sea state
2 and purple is for sea state 3.
The graphs are representing the noise level for every 1 Hz band at every occurring sea
state. The result is limited to frequencies higher than 16 Hz since the hardware of the
sensor had a high-pass filter with a cut off of 10 Hz. The curves all show high noise
levels in the band 100-1000 Hz, which falls in the band where ship is dominating the
noise. This is also in line with the results presented by Wenz. A small offset compared
to his results is seen in frequency, which probably is due to different sea conditions of the
measurements but it might also be due to the introduction of modern propulsion systems.
There is no significant difference between sea state 0.5 and 1 in the 100-1000 Hz band.
The wind speeds in sea state 1 is also low which means that there are no significant
increase of noise levels due to wind compared to sea state 0.5. The clear peak at 2000 Hz
for winter may be due to self-noise. This peak is not present in the summer, which may
be due to the sensor was switched.
The noise levels in summer are about 4-5 dB lower than in winter time. It could be a
result of the changed situation e.g. hydrographic characteristics, less shipping, more calm
weather.
In Wenz results an increase of noise level was observed for frequencies lower than 50 Hz.
This is not observed in this study. There might be an increase below 16 Hz but due to
the aforementioned hardware filter it cannot be observed. The lack of increase of noise
levels at lower frequencies were observed in earlier measurement in the Baltic Sea. A
plausible explanation is that the Baltic Sea is “shallower” than the definition used by
50
Wenz. The Baltic Sea average depth is about 55 m, while blue ocean studies regard 100
m as shallow. What the cause is for the observed continuous decrease of noise below 100
Hz is not known and has to be further investigated.
The law of fives also states that the noise level decreases with 5 dB per increasing octave.
Looking at sea state 2 the noise level at 500 Hz is about 79 dB and at 1000 Hz about 73
dB which is a decrease with 6 dB. The next octave has a noise level at 66 dB which is a
further decrease with 6 dB. At 4000 Hz the noise level is 65 dB and the law of fives seems
to be valid up to 2000 Hz. This may depend on the different conditions today compared
with the early sixties.
9.4
Shipping and ambient noise
The investigation in ambient noise dependency of shipping noise is dealt with in this
section. To analyse the influence on noise levels with ship traffic detailed information
on ship position and sound source level are required. The first is available through the
AIS-system, the latter is however unknown. In the first analysis the recorded noise levels
are plotted against the ship positions, see Fig. 9.13.
Figure 9.13: Distance to ships and noise levels in 10 Hz-10 kHz in Bothnian Sea in
January. Blue rings indicates ships noise levels at different distances to the sensor. The
solid lines indicates the mean noise levels included both distant and close shipping (red)
and only distant shipping (blue). The blue line is on top of the red.
51
The ship generated noise levels are dependent of the distance to the ship. This hypothesis
is valid up to a distance of about 5 km. Above 5 km there is none significant difference
of noise levels between different distances. Under 5 km the noise levels increases with
decreasing distance rapidly. A border of nearby shipping is set to 5 km and ships are
referred to as distant shipping. A test of the influence of the nearby shipping were performed and two values of mean are presented with the solid lines. The red line consists
of the mean for both nearby and distant shipping while the blue only consists of distant
shipping. There is almost no difference between them which means that at this location,
the nearby shipping has a very small influence on the ambient noise and the results presented earlier with Wenz curves and other meteorological based calculations are valid,
i.e. the results consists of noise from natural sources and distant shipping, i.e. ambient
noise.
There is a ship with a significant increase of noise level even at a distance above 19 km.
It is assumed that a ship passed within a close distance to the sensor, without an AIS
transmitter or with it turned off, in the same time as a distant ship did. This makes the
distant ship combined with the high noise level.
A notable feature is the ship, that was approaching the sensor between 22 and 15 km, with
an almost constant noise level of 118 dB. The recorded noise levels didn’t change even
when the ship was getting closer to the sensor. This phenomenon strongly suggests that a
single ship in the distant shipping zone does not influence the noise level but rather contributes to the ambient noise. A possible reason of why this particular ship had a higher
noise levels than the other in the zone is that the sea-state was higher during the passage.
By linear fit two lines for the data set, it is easier identify the noise dependency to distance. One line is fitted for the nearby ships, i.e. within 5 km distance, and one for the
distant field (above 5 km). The solid line presents the ship density. This means what
distance to the hydrophone was the most common. This was performed to verify the
classification of nearby- distance was done in a good manner.
52
Figure 9.14: Passing ships influence on the noise levels. The blue rings represent ships
noise level at different distances for 10 Hz-10 kHz in Bothnian Sea in January. The
dashed lines are linear fitted to the nearby and distant shipping data. The left dashed line
represent the nearby ships noise level dependency of distance to the sensor. Ships were
classed as nearby within 5 km from the sensor. Distant ships noise dependency of distance
is represented by the right dashed line. The ship distance distribution is represented with
the solid line, i.e. number of ships per distance unit.
The ship density peak is found above 15 km which first; makes it okay to use 5 km as
the border for nearby shipping in the calculations and second; makes the location suited
for ambient noise recordings.
It is clear how little the noise levels dependence on distance in the distant shipping field,
i.e. outside the 5 km range from the sensor. Outside this range the noise levels does
not change with the presents of a ship, but rather with other fluctuations in the ambient
noise. It is possible with this result to describe the soundscape of the Bothnian Sea. All
water volume farther than 5 km from a shipping line will have similar noise levels as the
levels outside the 5 km radius in Fig. 9.14.
The sea state influence of the ambient noise combined with ship induced noise. By colouring each ship representation with a sea state colour, it became possible to distinguish noise
levels for different sea states, which is presented in Fig. 9.15. The assumption of ambient
noise is ruling outside the nearby shipping border seems to be proven by this result.
53
Figure 9.15: The ships noise levels in 10 Hz-10 kHz at different distances from the sensor
and in what sea state. The solid lines indicate the mean noise level for each sea state.
Blue colour is sea state 1, orange is sea state 2 and yellow is sea state 3. It is calculated
on data from Bothnian Sea in January.
The result shows that there is a clear sea state dependence in the noise levels. Low sea
states are related to low noise levels and high sea states with high noise levels, as expected.
The conclusion is that the ambient noise is dependent on the sea state. If the ambient
noise is due to natural noise or a mixture of natural and anthropogenic noise cannot be
determined. Higher sea state can result in noisier ships as well as higher natural ambient
noise.
The mean noise levels for the different sea states in Fig. 9.15 are almost the same as presented earlier on meteorological noise dependency. This supports the theory that distant
shipping contribute to the ambient noise level, thus there is a constant shipping noise
always present.
In Wenz paper he stated that ship induced noise were dominating in the frequency interval 20 Hz to 1000 Hz. In this study this dependency was investigated by fitting two
lines, one for nearby and on for distant traffic, for different 1/3 octave bands, see Fig. 9.16.
54
Figure 9.16: Linear fittings of noise levels - distance data for 1/3 octave bands and noise
levels plotted against distance for 10 Hz-10 kHz. The data is for January at Bothnian
Sea. Rings indicates ships noise levels at different distances in 10 Hz-10 kHz. Green
dashed line is linear fitted for data in 10 Hz-10 kHz band. Red solid line is linear fitted
for data in 63 Hz, turquoise for 125 Hz, dashed purple for 500 Hz, dashed yellow for 2000
Hz and dashed black for 4000 Hz 1/3 octave band.
The analysis shows that the 63 Hz band has the lowest noise levels in the distant zone
but the strongest dependence on range in the nearby zone. The line of nearby shipping
for the 63 Hz 1/3 octave band starts at 120 dB. At the same level as for the 125 Hz and
500 Hz 1/3 octave bands. The implication is that the energy transmitted from a ship
in the 63 Hz 1/3 octave band is significant in the nearby zone but less important in the
distant zone. The 500 Hz 1/3 octave band shows the highest noise levels in the distant
zone, which is in line with Wenz result, that is ship contributes in the frequency band
400-800 Hz.
The 2000 and 4000 Hz 1/3 octave band both has a noise level beneath 110 dB in the
nearby zone. Their slopes are flat and the nearby zone change at short distance to distant
zone. Thus, noise levels at the high frequencies don’t contribute to the ambient noise
with any significance. The reason might be that ships do not generate sound at higher
frequencies, which corroborates with the Wenz curves in Fig. 9.11.
9.5
Range of passive sonar
Sonar systems are mainly used for specific purposes. Navies use them for detecting other
vessels. The detection range depends on the number of parameters that are described by
55
the sonar equation (cf. Eq. 7.1). In modern sonar systems arrays of hydrophone are used
to both increase detection ranges and to estimate bearing. In this study two specific targets were used; a corvette from post World War and a submarine. It is assumed that the
detection is done without a priory knowledge of the ship signature. Broad band detection
is used to increase the probability of detection since no clear signature is known. 1/3
octave bands are used both as a holdover from times when no digital signal processing
was possible but also due to the fact that 1/3 octave is neat to work with.
The source strengths were taken from Urick (1983) and Miasnikov (1995). Detection
ranges were established using the sonar equation earlier outlined in section 7.2. The
Detection threshold was set to 9 dB, the sensor is an LOFAR array consisting of 25 hydrophones resulting in a directivity index of 5 dB. The ambient noise levels were taken
from Fig. 9.11. Based on this data the detection ranges were established as a function
of sea state.
Fig. 9.17 shows the maximum range of detection for a corvette in cruising speed.
Figure 9.17: The maximum range of detection for a corvette in cruising speed in different
sea states at 100 and 1000 Hz for a passive sonar with a 25 m long hydrophone array for
Bothnian Sea in January. Dark blue is for sea state 0.5, blue is for sea state 1, green is
for sea state 2 and yellow for sea state 3.
These results is in line with the conclusions that a ship generates higher noise levels at
lower frequencies. It is apparently more efficient to “search” for a corvette at lower frequencies.
The results of the corvette can be directly compared to Fig. 9.18. It shows the corre56
sponding range for a quiet submarine. A quiet submarine is travelling in about 4 knots
at periscope depth. The submarine will choose a depth where the wave propagation plays
in the favour for covert operation. The hydrography shows that the optimal place is at
periscope depth according to Fig. 7.1 since the transmission loss was highest in the top
and the bottom layer.
Figure 9.18: The maximum range of detection for a submarine in quiet mode (4 knots) in
different sea states at 100 and 1000 Hz for a passive sonar with a 25 m long hydrophone
array for Bothnian Sea in January. Dark blue is for sea state 0.5, blue is for sea state 1,
green is for sea state 2 and yellow for sea state 3.
The submarines maximum detection range is about one per mille of the corvette. If the
submarine instead is travelling in 2 knots the detection range will be as Fig. 9.19.
57
Figure 9.19: The maximum range of detection for a submarine in very quiet mode (2
knots) in different sea states at 100 and 1000 Hz for a passive sonar with a 25 m long
hydrophone array for Bothnian Sea in January. Dark blue is for sea state 0.5, blue is for
sea state 1, green is for sea state 2 and yellow for sea state 3.
When the submarine is moving in 2 knots the maximum range of detection is even further
reduced with 90 per cent. In sea state 3 the maximum range is about 60 m for 100 Hz.
At these small values it may result in non-detection even if the submarine is closer than
60 m. Apparently using a LOFAR hydrophone is not effective for chasing submarines
while it will detect warships at relative large distances.
The maximum detection range is dependent on sea state and is doubled for sea state 3
compared to sea state 0.5. this scaling rule seems to be independent on source strength.
An interpretation of this in naval warfare tactics is that less care has to be taken to the
acoustic signature at higher wind speeds. The submarine can manoeuvre in a higher
speed in the same time as the surface going submarine hunters are suffering in performance due to the heavy weather.
Further, the results shows that there is no reason of listening at high frequencies using
this type of hydrophone. The detection ranges is four to five times longer at 100 Hz than
at 1000 Hz.
58
10
Conclusions
The location of the sensor was in the Bothnian sea at a depth of 63 meters. The sea contains brackish water and during summer a strong thermocline is present. During winter
normally an ice layer is covering the surface, but not during 2014 and all measurements
were done in ice free conditions. The sensor was deployed at about 15 km from the closest
shipping lane and the recordings consists of few samples with a nearby ship passing.
Wind speed is showed to correlate with noise levels. The averaged noise level in 10 Hz
to 10 kHz increased with sea state. A meteorological dependency of the noise level was
calculated and presented as an updated version of the Wenz curves for the Bothnian
Sea. Results were also presented proving that the wind direction and duration also is of
significant importance when analysing wind induced noise.
Ship induced noise is proved to be influencing the ambient noise in two situations. Nearby
ships has a strong but short lived influence while distant traffic noise always is present
in the Baltic Sea. The results showed that ships within 5 km from the hydrophone may
be regarded as nearby. Different 1/3 octave bands of ship noise were analysed and ship
noise above 2000 Hz showed no influence in the ambient noise. The most dominant 1/3
octave band in shipping noise proved to be the 500 Hz.
The sea states influenced the noise levels and it is showed that the range of a passive sonar
of type LOFAR is strongly dependent of the wind speed. An increase of sea state from
0.5 to 3, the maximum range of detection was half. Also a clear difference in maximum
range of detection was showed for 100 and 1000 Hz. Low frequencies transmits best in
water.
As a further investigation of this study some improvements could be done in the signal
analysis part. By introducing the KS-test as an indicator of what time windows that
was stationary it would be possible to filter out non-stationary signals. This could also
be used for ship detection since a ship passage would generate a non-stationary signal.
Only using stationary time windows the recordings would with better statistical validity
be the ambient noise. Also a another sensor would be good to use in a future study, to
measure the low frequencies that was not possible in this study. By introducing a better
sensor for low frequencies the hypothesis that the noise levels decreases with frequency
in the Baltic Sea could be falsified or proved.
Another improvement of the study would be to generate these results for other parts
of the Baltic Sea and if it would be possible, claim more accurate and other types of
meteorological data such as wind direction.
59
60
References
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62
Appendix A
A.1
About the project
BIAS
BIAS is an abbreviation for Baltic Sea Information on the Acoustics Soundscape and
refers to the project with the aim of solving the major challenges of Descriptor 11 in
the Baltic Sea. Descriptor 11 of the MSFD states: “’Introduction of energy, including
underwater noise, is at a level that do not adversely affect the marine environment.”.
The EU have presented their marine strategy framework for 2020 which states that the
oceans shall have a good environmental state and this includes that the underwater noise
can’t increase from now and that the level at 2020 is not that high that it causes harm
to marine animals habiting the Baltic Sea.
The reason behind Descriptor 11 is that ambient noise can mask the sound made by animals in the water which could cause missed opportunities of feeding, mating or detection
of predators. It has also been shown that fishes and whales is stressed due to a high level
of ambient noise, just as humans.
Further information about the BIAS project, please visit the web page
https://biasproject.wordpress.com.
A1
Appendix B
B.1
The location
Weather at the position
Figure B.1: Sea states occurring at the hydrophone location in February. The red line
indicates the mean noise level for all Sea States and the green line the median.
A2
Figure B.2: Sea states occurring at the hydrophone location in march. The red line
indicates the mean noise level for all Sea States and the green line the median.
Figure B.3: Sea states occurring at the hydrophone location in may. The red line indicates
the mean noise level for all Sea States and the green line the median.
A3
Figure B.4: Sea states occurring at the hydrophone location in June. The red line indicates
the mean noise level for all Sea States and the green line the median.
B.2
Hydrography of the location
Figure B.5: Hydrography in January- March for the location.
A4
Figure B.6: Hydrography in April- June for the location.
A5