1.
PMCC: MAIN FEATURES .......................................................................................................... 2
1.1
1.2
1.3
1.4
1.5
1.6
1.7
2
INTRODUCTION ......................................................................................................................... 2
DETECTION BY CORRELATION .................................................................................................. 3
TIME CORRECTION FOR A NON PLANAR ARRAY ........................................................................ 3
THRESHOLD OF DETECTION: THE CONSISTENCY ....................................................................... 4
THE PROGRESSIVITY ................................................................................................................. 4
CHECKING DATA QUALITY ........................................................................................................ 5
TIME FREQUENCY ANALYSIS AND POST-PROCESSING ............................................................... 5
DESCRIPTION AND TUNING OF THE MAIN PARAMETERS .......................................... 7
2.1
2.2
2.3
2.4
ARRAY PARAMETERS ................................................................................................................ 7
DETECTION PARAMETERS ......................................................................................................... 7
FREQUENCY PARAMETERS ........................................................................................................ 9
FAMILY PARAMETERS ............................................................................................................. 10
3
REFERENCES ............................................................................................................................. 11
4
APPENDIX ................................................................................................................................... 12
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1.
1.1
PMCC: MAIN FEATURES
INTRODUCTION
In contrast to a set of isolated sensors, a dense array, whose aperture is of the order of the wavelengths
of the signals of interest, allows similarity measurements of the recordings to avoid uncertainties
encountered with individual arrival-time picking.
Similarly to seismology, most of the infrasonic waves can be represented at a local scale by a set of


planar waves using the well-known relation f (r , t )  e i (t  kr )
where:
 2f
k 
is the wave vector associated to frequency f and phase velocity c
c
  2f 
2
is the angular frequency.
T
The frequency content of a recorded wave can easily be determined using a single station. At the

opposite, a set of sensors is needed to calculate the propagation parameter k . When the aperture of this
set of sensors is equivalent to some wavelengths of signal, this set is named an array. At the opposite,
when the aperture is much larger than the wavelength, it is named a network.
In the case of a network, the signal is often very different from one sensor to another and the measure
of the propagation parameters is derived from the set of arrival-times by inversion, as described by
Husebye (1969) in seismology. On the opposite, in the case of an array, we use the similarity of the
signals to compute arrival time differences using classical techniques of signal processing theory. This
set of arrival time differences is used to compute the propagation parameters with a Husebye’s derived
method.
The most classical method for estimating these wave parameters in the case of an array is a systematic
search in a specific domain of wave vector using the signals recorded on the sensors. For example the
 2f
disc defined in the wavenumber plane by the relation k 
corresponds to all the waves with a
V min
frequency f, with any azimuth and with a velocity V  Vmin . For each discrete wave vector of this
regularly discretized domain, the time delay at each sensor is calculated and the delayed signals are
summed. When the signals are mainly composed of random background noise, the energy variation of
the sum is small over the entire wave vector field. In contrast, if the signals are associated with a


specified vector k 0 , the energy obtained for k 0 will be much larger than for the other vectors. A lot of
methods have been proposed by different authors to find the wave vector which produces the
maximum energy [Capon, 1969]. This is not a trivial problem because data are discrete in the space
domain (i.e. only few sensors are used). This implies that for each frequency, false results can be
obtained due to correlated signals over one or more periods (ambiguity effect). To study these effects,

Capon suggested to compute the array beam forming function B( k ) 

 e ikr
j
. It represents the array
j
response for a wave which arrives vertically under it, i.e. with a horizontal planar wave reaching all
the sensors at the same time.
The phase velocity of the wave measured is then infinite, which leads to

a maximum amplitude for k  0 .
The main assumption linked to this type of methods is the search of a signal modeled by a planar wave
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recorded on all the sensors of the array. In practice, some of these assumptions are confirmed to
various extents as diverse sources cause interference with these signals. That is why a more flexible
method, less constraining with respect to the model, is proposed. It is based on conventional signal
processing techniques to detect a stable signal on two or more records, partly by relaxing the planar
wave model rigidity. The PMCC method (Progressive Multi-Channel Correlation), originally designed
for seismic arrays, proved also to be efficient for analyzing low-amplitude infrasonic coherent waves
within non-coherent noise.
1.2
DETECTION BY CORRELATION
A temporal signal s(t ) can be represented by its Fourier transform S ( f )  A( f )e i ( f ) where A( f )
represents the spectral amplitude and  ( f ) is the phase. The background noise is characterized by a
rapid variation of both A( f ) and  ( f ) from one sensor to another, even if they are closer than one
wavelength of signal. On the opposite, in case of signal propagating between the sensors, the
following relations are available:
A2 ( f )  A1 ( f )
 
 2 ( f )  1 ( f )  (r2  r1 )
These relations indicate that no deformation exists between the two signals, and that the only
  
 
difference is a delay depending on the relative positions of the sensors (i.e.: (r2  r1 )  k (r2  r1 ) in
the case of a planar wave).
Based on these two observations, a signal-processing tool can be used to detect a signal present on the
records si (t ) and s j (t ) . The correlation function is used to measure the time delay t ij between the
two records. In case of a wave propagating without distortion (assuming a planar wave), this delay is
the same for all frequencies of the signals:
tij 
1
( j ( f )  i ( f ))
2f
This measurement is made in the time domain with values ranging from -1 to 1. Taking into account
all frequencies, it measures in a given time window the similarity of the signals when shifted in time.
The maximum of the correlation function gives the time delay between the signals. This method
enables a decision to be made on whether there is a signal in a set of simultaneous records,
independently of any information on previous records.
1.3
TIME CORRECTION FOR A NON PLANAR ARRAY
In some cases, for a non planar array, the travel time differences due to elevation differences between
sensors become not negligible. For such arrays, the elevation of each array element should be taken
into account. The calculation of the time delays between two sensors is to be improved. It can be
shown that the time delays then also depends on the local sound speed and the elevation angle. In a
first order, this correction is applied:
j 
 x sin(  )  y cos( ) z cos(i)

VT
c
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where:
 is the backazimuth (often called azimuth), angle of wave front approach, measured clockwise
between the north and the direction towards the source,
c is the local sound speed
d is the horizontal distance between sensor j and the barycenter
j is the time-delay between sensor j of coordinates (x,y,z) and the center of the array (0,0,0)
i is the incidence angle between the direction of propagation of the wave front and the vertical
VT  sin( i )
1.4
d
, is the horizontal trace velocity across the array.
c
THRESHOLD OF DETECTION: THE CONSISTENCY
To avoid ambiguity problems when correlating the records from sensors too far apart, the analysis is
initialized on the smallest groups of three sensors. The correlation function is used to calculate the
propagation time tij of the wave between sensors i and j. For each subnetwork (i,j,k), the closure
relation  tij +tjj+tki = 0 should be obtained. In the presence of background noise the phase is
unstable. Therefore, the delays measured in this case are the result of random phase combinations.
These delays, independent of the amplitude of each elementary wave, become random, and the closure
relation given above is no longer valid.
The consistency of the set of delays obtained using all the sensors of R n is then defined as the mean
quadratic residual of the closure relations:
rijk  tij  t jk  tki


6
2 i, j , k  Rn
cn 
r
 ijk
n(n  1)( n  2) i  j  k 

If this consistency is below a given threshold cThreshold , a detection is observed on R n .
1.5
THE PROGRESSIVITY
To minimize errors in the calculation of the wave parameters, distant sensors are progressively added
using a criterion based on a comparison between their distance to the subnetwork and the computed
wavelength. This progressive use of distant sensors has two main effects: the removal of false
detections which could be due to correlated noise at the scale of the starting subarrays, and a better
estimation of the wave parameters by increasing the array aperture.
After being initialized with a small subnetwork of three sensors, in order to avoid ambiguity problems
inherent in the correlation of signals from distant sensors, the wave parameters calculated on the initial
subnetwork R n are used when adding other sensors. For that, a propagation of a planar wavefront is
assumed. The new measured time delay is given by the maximum of the correlation function which is
the closest to the one that has been estimated. Each elementary detection is therefore defined by
several parameters such as the consistency value, the number of sensors participating to the detection,
the frequency, the horizontal trace velocity and the backazimuth.
Such a detector is independent of the signal amplitude and uses only the intrinsic information of the
recordings. As long as the closure relation is valid, the use of sensors increasingly further apart gives
more precise wave parameters since the aperture of the network increases with each new sensor. The
final solution is given by the largest subnetwork.
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1.6
CHECKING DATA QUALITY
In the PMCC configuration file (see in Appendix), a list of starting subnetworks of three sensors is
defined. As explained previously, the detection is initiated through each of these subnetworks. Then,
as long as there are available sensors and the consistency value remains lower than its threshold, new
sensors are aggregated. To avoid wrong results due to the lack of data in the recordings, an automatic
procedure checks the data quality. If the initial subnetworks contain sensors with consecutive zeros in
the recordings, this procedure looks for other set of three sensors belonging to the array. Among all
possible combinations calculated from the remaining sensors, the best subnetworks are selected. The
principle is to sort them according to symmetry and size criteria. Equilateral triangle of small aperture
is the best configuration. The maximum number of new eligible subnetworks corresponds to the
number of subnetworks defined in the configuration file.
1.7
TIME FREQUENCY ANALYSIS AND POST-PROCESSING
The processing is performed consecutively in several frequency bands fi, and in adjacent time
windows tj covering the whole period of analysis. To avoid unrealistic wave detection, a further
condition is introduced. A set of several elementary detections in the time-frequency domain is
considered to represent one detected wave (corresponding for example to different frequency bands or
adjacent windows). Conversely, several waves with different parameters may coexist in the same time
window but in different frequency bands. Each wave must be identified separately. To do this, a
nearest-neighbor search of elementary detections in the (ti,fi,VTi,i) domain is used [Cansi, 1995;
Cansi, 1997]. The final detection is thus an aggregate of close-enough points in this domain. Different
distances can be used to connect close-enough points, but the more usual is a weighted Euclidian
distance defined by:
The  ’s are used as weighting factors to allow the comparison of quantities of different meanings.
These weights can be tuned independently. As opposed to the other weights,  V is the only
dimensionless parameter. The velocity weight is expressed in percent. Figure 1 presents an example of
PMCC results before and after this processing.
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Figure 1 - Results of PMCC calculation on signals generated by a gaz explosion in Belgium recorded
at the Flers experimental infrasound station set up in Normandy (France). The PMCC results
(horizontal trace velocity and azimuth) are presented in time / frequency diagrams. Values are given
according to the color scales. The results are presented from 0.1 to 4 Hz in 10 equally spaced
frequency bands. Azimuths are given clockwise from North. Top: elementary detection. Bottom: final
results after the post-processing,
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DESCRIPTION AND TUNING OF THE MAIN PARAMETERS
Depending on type of signals to be processed (eg waveforms and frequency content) and the
background noise level, the PMCC parameters have to be set in order to ensure an optimum detection
providing a compromise between a low detection threshold and low numbers of false detections. Too
large threshold values will allow a low level of detection, but a large number of false detections are
expected. On the opposite, too small values are not appropriate to detect coherent waves with poor
signal-to-noise ratio, but a very few number of false detections will be obtained.
PMCC requires input of two configuration files: pmcc.ini and filters.ini. These contain, respectively,
the computation parameters and the filter coefficients. Examples of these two files as well as the
format of the result files are provided in the Appendix. The main PMCC parameters are related to the
array, the detection, the filtering and the post processing. They are described below along with some
guidelines underlying the choice of suitable values.
2.1
ARRAY PARAMETERS
1. [X,Y] are the sensor coordinates (relative to the barycenter, in km). For a relevant calculation
of the wave parameters, they should be accurate enough regarding the sampling rate, the array
aperture and the wavelength of the signals.
2. NbSensors: number of sensors of the arrray.
3. NbSubNet is number of subnetworks to be processed. In order to avoid ambiguity problems
inherent in the correlation of signals from distant sensors, the calculation is generally initiated
with small subnetworks of three sensors. Then, for each subnetwork, distant sensors are
progressively added using a criterion based on a comparison between their distance to the
subnetwork and the computed wavelength. This progressive use of distant sensors has two
main effects: the removal of false detections which could be due to correlated noise at the
scale of the starting subarrays, and a better estimation of the wave parameters by increasing
the array aperture. Since all sensors of the array will be added as long as the consistency
remains a threshold value, it is not necessary to define all available subnetworks. Furthermore,
the computation time grows linearly with NbSubNet. For an 8-element array, a maximum
reasonnable number of NbSubNet is 5. If the aperture of the full-array is significantly smaller
than the wavelength, in order to make the process less sensitive to highly correlated
undesirable signals, the largest subnetworks can be defined.
4. GeoSubNet describes each selected subnetwork, with numbers referring to the sensors. Their
aperture should be in the same order of wavelength of the signals and their geometry should be
as symetric as possible. The equilateral triangle is the optimum configuration.
5. DelayCorrection (s) is the time correction to be applied to each channel (null is the default
value).
2.2
DETECTION PARAMETERS
1. WindowLength (s) is the length of time window to compute the correlation. It can not be lower
than the maximum propagation time of the wave between two sensors. This value is generally
increased to ensure a smoothing of the time base, which significantly reduces the number of
false detections and improves statistics on the measurements of the wave parameters. A
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reduction in the number of families (final detection of PMCC aggregating close-enough pixels
in the time / frequency / speed / azimuth domain) is then expected, providing a synthesis of
information. In the case of long duration phenomena such as microbaroms or MountainAssociated Waves, WindowLength can contain several periods (up to 10). In case of transient
signals, WindowLength can also be greater than the maximum propagation time between
sensors. The disadvantage of a broad time window is the anticipation of the detection and the
increase of its duration. In order to take into account this effect, a time picking procedure has
to be used for an accurate arrival time measurement.
2. TimeStep (s) is the time shift between two consecutive windows. It cannot exceed
WindowLength. It defines the time width of the elementary detection in the time / frequency
domain, also named pixels. A low value for TimeStep allows to monitor in greater detail the
time variation of the wave parameters without interfering with the detection performance.
Reducing TimeStep by a factor of 2 proportionately increases the number of pixels per family.
In order to maintain a similar level of detection using TimeStep/2, ThresholdLFamMin must
be multiplied by two and the standard deviations Sigma_t, Sigma_f, Sigma_v and Sigma_a can
be reduced (see below).
3. ThresholdConsistency (s) is the maximum consistency threshold for detection. This parameter
is the first criterion to reject a pixel, and the choice of its value has a major impact on the
detection performance. ThresholdConsistency has to be higher than the sampling period,
otherwise the closure relation will never be fullfilled and no detection will be obtained. In
unfavorable background noise conditions, including the consequent interferences in the intercorrelation functions, ThresholdConsistency must be raised. In addition, too high a value will
noticeably increase the number of false detections linked to the incoherent nature of the
background noise. The tuning of this value is achieved taking into account the frequency band
being studied and the mean noise level prevailing at the station
4. Q defines the threshold distance for integration of a sensor in the current subnetwork. There is
integration if the distance between the sensor and the barycenter of the subnetwork is lower
than Q. This threshold is useful to remove far-away sensor for which poorly-correlated
signals are expected. If the maximum distance between sensors is not too large compared to
the wavelength, Q can be set to a high value (typically 50). In that case, all sensors will be
used for the calculation.
5. ThresholdNbSensors is the minimum number of sensors participating to a detection. It ranges
between 3 and the number of elements in the array. A high level of detection is obtained with
a large value of ThresholdNbSensors.
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2.3
FREQUENCY PARAMETERS
1. NbFilters defines the number of bands available to be filtered. For a given frequency range,
NbFilters also defines the frequency width of the pixels in the time/frequency domain.
Typically, for frequency range of one decade, a reasonable number of NbFilters is 10.
2. NbCoeff defines the number of AR filter coefficients available in the file filters.ini.
3. DT_Band defines the index of the filter bands to be processed (numbered from 0 to NbFilters)
4. Decim is the decimation factor. The tuning of this parameter must take account the frequency
band to be processed in relation to the sampling frequency. As show by the figure below, a
filter with a too narrow band produces instability in the filter frequency response, which will
disturb detection performance. Typically, for a bandwidth of 0.5 Hz and a sampling frequency
of 20 Hz, Decim=1. For a bandwidth of 0.05 Hz, Decim=5 provides a good compromise.
[0.005-0.01] Hz
Fe = 20 Hz – Decim = 1
[0.005-0.01] Hz
Fe = 20 Hz – Decim = 10
0.005-0.01] Hz
Fe = 20 Hz – Decim = 50
Figure 2 - Digital filter frequency response as a function of the sampling frequency (Chebyshev order:
2).
In the current version of PMCC, a unique time window length can be defined. In order to cover a
frequency band larger than one decade, it is preferable to split the processing in several bands and
to adjust the time window length according to bands. The three bulletins are then merged for the
purposes of statistical analysis and the creation of the graphs contained in the present report.
For infrasound monitoring in the [0.05-5] Hz band, three bands can be defined:
 Band 1: The [0.5-5] Hz band dedicated to detection of transient signals whose origin may
be natural or man-made, and which may propagate over distances of several hundred
kilometers (Decim=1, WindowLength=30 s).

Band 2: The [0.05-0.5] Hz band dedicated mainly for the detection of microbaroms and
possible remote large events such as explosions, meteorites (Decim=5, WindowLength=90
s).

Band 3: The [0.005-0.05] Hz band dedicated to detection of atmospheric waves such as
Mountain Associated Waves or gravity waves (Decim=20, WindowLength=200 s).
The fairly low order of the filters used (order 2) allows an overlap of detections among these three
frequency bands; for example, microbaroms are sometimes detected in Band 1, and Mountain
Associated Waves in Band 2.
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2.4
FAMILY PARAMETERS
1. ThresholdDistance allows the integration of one candidate pixel into one growing family selected
among the existing ones. It is a dimensionless threshold defining a maximum acceptable
distance in the time / frequency / speed / azimuth domain. A family is a candidate if at least
one of its pixels has distance to the candidate pixel which is lower than ThresholdDistance.
Since it is a normalized distance, an acceptable value is one.
2. ThresholdDate (s) defines the maximum standard deviation in time between a pixel and a
family. It must be higher than TimeStep, otherwise no integration can occur. This is because
the distance in time t between all candidate pixels and the family cannot be less than
TimeStep, yielding to a ratio t/Sigma_t always greater than one (typical value of
ThresholdDistance).
3. Sigma_t (s) defines the maximum distance in time between one pixel and a family. It must be
higher than TimeStep, otherwise no integration can occur. If there is no more pixel to be
integrated because of this condition, the family is closed.
4. Sigma_f (Hz) defines the maximum standard deviation in frequency. It must be greater than
the frequency resolution of one pixel, eg (Fmax-Fmin)/NbFilters. If this is not the case, the
distance in frequency between a candidate pixel and an existing family will always be greater
than the value for ThresholdDistance.
5. Sigma_v (%) and Sigma_a (°) define the maximum standard deviation in velocity and
backazimuth, respectively, for the integration of a plot into a family. These values should be
larger than the expected physical and numerical resolution of the wave parameters of the
detected signals. For the processing in a low frequency band (wavelength significantly greater
than the array aperture), these values should be in increased.
6. ThresholdLFamMin (number of pixels) any closed family whose length is less than this
threshold is removed.
7. ThresholdLFamMax (number of pixels) prevents memory troubles with families growing
infinitely. It limits the extention of all families whose size extends beyond its threshold. In that
case, the families are closed. ThresholdLFamMax is defined as a number of pixels and can be
set to 1000. In the case of permanent detections generating by long duration phenomena
(microbaroms for example lasting frequently from hours to days), the number of pixels per
family quickly reaches ThresholdLFamMax. Then, contiguous families with similar
propagation parameters will be separated despite the continuous nature of the signals.
8. VstoreMin and VstoreMax (km/s) are the minimum and maximum values and the average
horizontal trace velocity of the family. If the calculated value is out of this range, the family is
removed. These parameters allow to suppress false detections based on unrealistic values of
the velocity according to know propagation models.
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3
REFERENCES
Capon, J, High resolution frequency wavenumber spectrum analysis, Proc. IEEE, 57, 1969.
Cansi, Y., An automatic seismic event processing for detection and location: the PMCC method,
Geophys. Res. Lett., 22, 1021-1024, 1995.
Cansi Y. and Y. Klinger, An automated data processing method for mini-arrays, CSEM/EMSC
European-Mediterranean Seismological Centre, NewsLetter 11, 1021-1024, 1997.
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4
APPENDIX
Some examples of the format of the initialiation and result files of PMCC are provided. In addition to
the parameters described above, the followings have to be defined:
Signal parameters:
- Fe: sampling frequency (Hz)
- FilePath: directory containing the signal files to be read
- FileNames: signal file names to be read
- TypeFiles: format of signals to be read [Ascii: 0 – Fonyx: 1 – Css: 2]
- TypeCss: Css format
- FoffCss: read offset for Css format
File parameters:
- FilePath: directory containing the signal files to be read
- FileNames: names of signal files to be read
- ResultFile: full path for the results file
- FamiliesFile: full directory path for the family file
- BulletinFile: full directory path for the bulletin file
- FiltersFile: full directory path for the file containing the filter parameters
- SaveIntermResults: saving intermediate results (0/1)
- SaveBands: saving frequency bands
- [SaveBeginDate, SaveEndDate]: saving the time window
Detection parameters:
- StoreRes: saving result file (0/1)
- StoreFam: saving family file (0/1)
- StoreBul: saving bulleting file (0/1)
- ClassFile: path for use with fuzzy classification methods
- StoreClass: =0  indicates that the latter methods are not implemented
Fe = 20.000000
Decim = 1
WindowLength = 30.000000
WindowGap = 5.000000
StartingDate = 0,0,0,0
EndingDate = 14400,14400,14400,14400
q = 50.000000
ThresholdConsistency = 0.300000
ThresholdNbSensors = 3
NbSensors = 4
NbFilters = 10
NbCoeff = 4
FilePath =
"U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\"U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\"U:\Doc\R
FP\Task4\I33mg\02_08_2002_00_04\"U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\"
FileNames =
"ArchI33MG.1013126400.w"ArchI33MG.1013126400.w"ArchI33MG.1013126400.w"ArchI33MG.1013126400.
w"
TypeFiles = 2
TypeCss = s3
FoffCss = 0,864000,1728000,2592000
StoreFam = 1
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StoreRes = 1
StoreBul = 1
StoreClass = 0
VStoreMin = 0.100000
VStoreMax = 1000.000000
FamiliesFile = "U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\hf\families.txt"
BulletinFile = "U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\hf\bulletin.txt"
FiltersFile = "U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\hf\filters.ini"
ResultFile = "U:\Doc\RFP\Task4\I33mg\02_08_2002_00_04\hf\results.txt"
ClassFile = "new_regle.fis"
NbSubNet = 4
X = -0.016474, 0.63416, 0.25469, -0.87238
Y = -0.025133, 0.94778, -1.4259, 0.50321
GeoSubNet = {0,1,3}{0,2,3}{1,2,3}{0,1,2}
Bands = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
ThresholdDate = 10.000000
ThresholdLFamMin = 7.000000
ThresholdLFamMax = 300.000000
ThresholdDistance = 1.200000
sigma_t = 10.000000
sigma_f = 1.000000
sigma_v = 0.030000
sigma_a = 0.200015
OPTION = 1
DelayCorrections = 0.0, 0.0, 0.0, 0.0
SaveIntermResults = 0
SaveBands = 1, 2, 3
SaveBeginDate = 30.0
SaveEndDate = 60.0
Table 1 – Format of the pmcc.ini file.
The following parameters must be defined in the configuration file filters.ini:
- [Fmin, Fmax]: low and high cut-off frequencies for each filter (reduced frequencies
standardized at Fe/2),
- [ForwardCoeffs, ForwardCoeffs]: AR filter coefficients.
Fmin = 0.005, 0.0245, 0.044, 0.0635, 0.083, 0.1025, 0.122, 0.1415, 0.161, 0.1805
Fmax = 0.0245, 0.044, 0.0635, 0.083, 0.1025, 0.122, 0.1415, 0.161, 0.1805, 0.2
ForwardCoeffs
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
0.0298513, 0,
=
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
-0.0597026,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.0298513
0.0298513
0.0298513
0.0298513
0.0298513
0.0298513
0.0298513
0.0298513
0.0298513
0.0298513
ReverseCoeffs =
-3.45563, 4.49686, -2.62477, 0.58356
-3.39048, 4.38357, -2.57528, 0.58356
-3.27449, 4.18722, -2.48718, 0.58356
-3.1094, 3.91956, -2.36178, 0.58356
-2.8977, 3.59657, -2.20098, 0.58356
-2.64255, 3.23755, -2.00718, 0.58356
-2.34778, 2.86396, -1.78328, 0.58356
-2.01781, 2.4981, -1.53265, 0.58356
-1.65759, 2.16185, -1.25904, 0.58356
-1.27252, 1.87528, -0.966556, 0.58356
Table 2 – Example of format of the filters.ini file (Fe=20 Hz, Decim=1, Fmin=0.1 Hz, Fmax=4
Hz,NbFilters=10).
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Main Features and tuning parameters
The results files generated are result.txt and families.txt.
The result.txt file contains nine columns. Each line corresponds to an elementary detection (pixel) in
the time/frequency domain. The ninth column is not read.
- TPS: date (s), relative time at beginning of file
- FRQ: central frequency (Hz)
- AZI: backazimuth (rad)
- VIT: trace velocity (km/s)
- CONS: consistency (s)
- NBST: number of channels contributing to the detection
- RMS: amplitude (Pa/ Hz)
- COR: correlation coefficient (dimensionless)
- CHAN: sensors contributing to the detection
15.00
15.00
15.00
20.00
20.00
25.00
25.00
25.00
25.00
30.00
30.00
(…)
0.29500000
0.68500000
1.07500000
1.85500000
2.24500000
0.68500000
1.85500000
2.24500000
3.80500000
1.07500000
1.46500000
4.246
2.136
0.441
0.719
0.953
3.869
0.953
0.953
0.956
3.673
0.933
4.740445 0.0791
40.228505
31.045538
3.445694 0.0354
0.351246 0.0000
0.206430 0.0500
0.351246 0.0000
0.351246 0.0000
0.350808 0.0354
0.241774 0.0500
0.363258 0.0000
4
0.0500
0.0500
4
4
3
4
4
4
3
3
0.00899230
0.73
3
0.00302511
3
0.00189505
0.00031060
0.27
0.00031761
0.4
0.00064196
0.30
0.00032001
0.43
0.00033106
0.55
0.00036291
0.58
0.00039923
0.35
0.00034552
0.33
|0|1|3|2
1.18
|0|1|2
0.51
|0|1|2
|1|2|3|0
|0|1|2|3
|0|1|3
|0|1|2|3
|0|1|2|3
|0|1|2|3
|0|1|2
|0|1|3
Table 3 – Format of the result.txt file.
The families.txt file contains the following seven parameters:
- TPS: date (s), relative time at beginning of file
- FRQ: central frequency (Hz)
- AZI: backazimuth (rad)
- VIT: trace velocity (km/s)
- RMS: amplitude (Pa/ Hz)
- COR: correlation coefficient
- CONS: consistency (s)
Each line in families.txt is the result of a selection of elementary detections contained in result.txt.
The file families.txt lists homogeneous groups – the ‘family’ concept – within which the detections
are sufficiently close neighbors in the time/frequency/speed/azimuth domain. The formation of
families is dependent on the Detection Parameters defined in pmcc.ini. Each family is separated in
the file by a dotted line. The file provides backazimuth and speed statistics for each family (mean
value and standard deviation).
-------------------------------------date: 200.00 s freq: 1.075 Hz azim: 0.93272 rads sp: 0.34248 km/s RMS: 0.00037377 Pa cor: 0.27 cons: 0.000
date: 30.00 s freq: 1.465 Hz azim: 0.93272 rads sp: 0.36326 km/s RMS: 0.00034552 Pa cor: 0.33 cons: 0.000
date: 15.00 s freq: 2.245 Hz azim: 0.93272 rads sp: 0.36326 km/s RMS: 0.00093410 Pa cor: 0.53 cons: 0.000
date: 15.00 s freq: 2.635 Hz azim: 0.95294 rads sp: 0.35125 km/s RMS: 0.00082642 Pa cor: 0.49 cons: 0.000
date: 15.00 s freq: 3.025 Hz azim: 0.95294 rads sp: 0.35125 km/s RMS: 0.00075597 Pa cor: 0.48 cons: 0.000
date: 15.00 s freq: 3.415 Hz azim: 0.93272 rads sp: 0.36326 km/s RMS: 0.00069941 Pa cor: 0.57 cons: 0.000
Azimuth mean: 0.948
Speed mean: 0.354698
Azimuth standard deviation: 0.01281
Speed standard deviation: 0.00585810
-------------------------------------(…)
Table 4 – Format of the families.txt file.
The detections are grouped together in a bulletin file containing the following ten parameters:
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PMCC Documentation
Main Features and tuning parameters
NAME
S
T
S
SSD
A
ASD
fmin
fmax
RMS
Cons
Cor
N
Description
Origin time (UT)
End Time (UT)
Mean propagation speed (km/s)
Speed standard deviation (km/s)
Mean backazimuth (deg)
Backazimuth standard deviation (deg)
Minimum frequency for family (Hz)
Maximum frequency for family (Hz)
Amplitude RMS (Pa/ Hz)
Consistency mean value (s)
Correlation mean coefficient
Number of plots per family
Table 5 – List of the result parameters.
OT: Origin Time (UT)
ET: End Time (UT)
S (km/s): Speed (km/s)
SSD (km/s): Speed Standard Deviation
A (°): Azimuth (deg)
ASD (°): Azimuth Standard Deviation
fmax (Hz): Minimum frequency (Hz)
fmin (Hz): Maximum frequency (Hz)
RMS (Pa/V Hz): Root Mean Square (Pa)
CONS (s): Consistency Mean Value
COR: Correlation Mean Coefficient
N: Number of elements of the detected family
====================================================================================
OT AND S SSD A ASD fmin fmax RMS CONS COR N
2002-01-25
2002-01-25
2002-01-25
2002-01-25
2002-01-25
2002-01-25
2002-01-25
(…)
02:42:40
02:42:45
02:43:15
02:45:00
02:44:30
03:50:25
07:13:00
2002-01-25
2002-01-25
2002-01-25
2002-01-25
2002-01-25
2002-01-25
2002-01-25
02:43:00
02:43:05
02:43:50
02:45:15
02:45:20
03:50:40
07:14:35
0.382
0.355
0.329
0.313
0.335
0.313
0.328
0.007
0.003
0.004
0.005
0.011
0.006
0.004
8.0 1.1 3.415 3.805 99.129 0.12 0.40 9
359.5 0.3 0.295 3.025 211.996 0.05 0.45 29
359.6 1.7 0.295 2.635 264.880 0.09 0.37 25
356.5 3.3 1.855 3.025 1204.654 0.06 0.35 10
0.8 0.8 0.295 1.075 795.344 0.09 0.48 20
330.1 1.5 0.295 0.685 138.614 0.12 0.57 7
310.7 2.0 0.295 3.805 229.810 0.05 0.53 119
Table 6 – Format of the bulletin.txt file.
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PMCC Documentation
Main Features and tuning parameters
Table 7 – PMCC flowchart describing the detection process.
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