GNGTS – Atti del 19° Convegno Nazionale / 12.10
A. Ursino (1), H. Langer (1), L.Scarfì (1), G. Di Grazia (1) and S. Gresta (1,2)
(1)
(2)
Sistema Poseidon, C.da Cava Sorciaro, Priolo-Gargallo (SR)
Dipartimento di Scienze Geologiche, Università di Catania, Corso Italia, Catania
DISCRIMINATION OF QUARRY BLASTS FROM TECTONIC
EARTHQUAKES IN THE IBLEAN PLATFORM
(SOUTHEASTERN SICILY)
Abstract. The seismic network set up in the Iblean Plateau (Southeastern Sicily) in the framework of
the POSEIDON project is aimed at the seismic surveillance of the zone, and in particular the
identification of faults with enhanced activity. The seismic activity as inferred from the records of local
events recorded in the time span September 1999-February 2000 showed an apparent concentration
of events in the zone between Augusta and Siracusa. However, the heterogeneity in the distribution of
events with daytime suggested that the seismicity maps are severely biased by artificial events, such
as quarry explosions. We have distinguished between tectonic earthquakes and quarry blasts by the
inspection of waveforms of certain key stations, and by spectral analysis. As a general rule we found
that the local tectonic microearthquakes are richer in high frequencies than the quarry blasts. We
tested our discrimination based on waveforms and spectra with a data of local events recorded in the
time span September 1999-February 2000. All events identified as quarry blasts have occurred during
daytimes between 08:00 a.m. and 03:00 p.m. GMT and on weekdays from Monday to Friday.
Automatic discrimination was carried out in a straightforward way using artificial neural networks
(ANN) in a supervised classification. The application of the ANN to our data set gave a success of
about 95%.
DISCRIMINAZIONE DI SCOPPI IN CAVA DA TERREMOTI TETTONICI NELLA PIATTAFORMA
IBLEA (SICILIA SUD-ORIENTALE)
Riassunto. La rete sismometrica installata nella piattaforma iblea (Sicilia sud-orientale) nell'ambito del
progetto POSEIDON è finalizzata alla sorveglianza sismica dell'area ed in particolare all'identificazione
di faglie attive. La sismicità registrata nel periodo settembre 1999-febbraio 2000 mostra un'apparente
concentrazione di eventi nella zona fra Augusta e Siracusa, ma il loro aumento diurno suggerisce la
presenza di eventi artificiali, come scoppi in cava. Gli eventi tettonici sono stati separati da quelli
artificiali tramite l'analisi spettrale delle forme d'onda registrate da alcune stazioni. Abbiamo trovato
che, in generale, i microterremoti tettonici presentano maggior contenuto di alte frequenza e che gli
scoppi avvennero tra le 8 e le 15 dei giorni lavorativi. È stata definita una discriminazione automatica
degli scoppi tramite l'uso di reti neurali artificiali che ha dato ottimi risultati.
INTRODUCTION
Southeastern Sicily, in particular the Iblean Plateau, belongs to the zones in
Europe with the highest seismic potential. During the past millenium Southeastern
Sicily was struck by several strong earthquakes (Fig. 1), which reached magnitudes
of about M = 7.0 and caused some 10,000 deaths each. Actually, the Ioanian coast
belongs to the most populated areas in Sicily, while between the cities of Augusta
and Siracusa there are important industrial petrochemical facilities bearing an
elevated secondary seismic risk.
In the light of the considerable seismic potential, a permanent seismic network
in the Iblean Plateau was set up in the framework of the POSEIDON project. This
network actually consists of 9 digital three-components stations (Fig. 1) and is aimed
at the seismic surveillance of the zone, and in particular the identification of faults
GNGTS – Atti del 19° Convegno Nazionale / 12.10
with enhanced activity. The earthquakes recorded during the operation time of the
network are of small magnitudes which did not exceed M = 4.0 yet. Despite the
modest seismic energy release, the distribution of hypocenters may give useful
seismotectonic hints such as the existence of active faults, orientation of seismic
dislocations, the relation of seismicity to tectonic structures visible at the surface, and
so on.
The geometrical distribution, the technical specifications of the network together
with the low noise level recorded at the stations permit a sufficient completeness of
the catalog for events whose epicenters fall within the area represented shown in Fig.
1 and whose magnitudes are greater than 1.0. The errors of hypocentral coordinates
calculated with the program HYPOELLIPSE (Lahr, 1989) are typically of the order of
2 km (or less) for the epicenter, and 5 km (or less) for the focal depth (Bollettino
Sistema Poseidon, 2000).
SEISMICITY DISTRIBUTION IN TIME AND SPACE
The seismicity map in the Iblean Plateau inferred from events recorded in the
time span September 1999-February 2000 (Fig. 2) showed an apparent
concentration of activity along the coastal zone between the towns of Augusta and
Siracusa. This heterogeneity makes us suspect that the data set is affected by the
presence of artificial events, in particular quarry blasts. Quarry blasts are supposed
to occur more frequently during working hours of the day. We therefore may
investigate possible biases introduced to seismicity maps by filtering the data set with
respect to day-time.
SR2
SR1
1169
Lentini
A
SE
< 6.5
< 6.0
< 5.5
< 5.0
< 4.5
N
M
M
M
M
M
A
NI
1693
7.0 <= M < 7.5
6.0 <=
5.5 <=
5.0 <=
4.5 <=
4.0 <=
IO
MAGNITUDE
1990
SR9 Augusta
SR3
1542
SR4
SR6
SR5
Siracusa
BL
EA
N
PL
AT
EA
U
1693
I
SR8
SR7
Ragusa
SICILY
CHAN
NEL
0
10
20
Km
Fig. 1 - Historical seismicity (open dots) in Southeastern Sicily. Epicenter coordinates were taken from
Azzaro and Barbano (2000), coordinates of the event of 13 December 1990 from Amato et al. (1995).
Black triangles represent station of the seismic network.
GNGTS – Atti del 19° Convegno Nazionale / 12.10
AN
NI
IO
A
SE
Lentini
O
RM
Augusta
IB
LE
AN
PL
A
TF
Siracusa
Ragusa
SICILY
CHAN
10
0
NE L
20
Km
Fig. 2 - Overall “seismicity” in the time span September 1999-February 2000.
AN
NI
IO
A
SE
Lentini
Siracusa
IB
LE
AN
PL
AT
FO
RM
Augusta
SICILY
CHANN
EL
Ragusa
0
10
20
Km
Fig. 3a - Distribution of the overall seismicity in the time span September 1999-February 2000
occurred between 03:00 p.m. and 08:00 a.m. All hours are in GMT. The hammer symbols give the
location of quarries as reported by the Distretto Minerario of Catania.
GNGTS – Atti del 19° Convegno Nazionale / 12.10
AN
NI
IO
A
SE
Lentini
Siracusa
IB
LE
AN
PL
AT
FO
RM
Augusta
SICILY
Ragusa
CHANN
EL
0
10
20
Km
Fig. 3b - Distribution of the overall seismicity in the time span September 1999-February 2000
occurred between 08:00 a.m. and 03:00 p.m. All hours are in GMT. The hammer symbols give the
location of quarries as reported by the Distretto Minerario of Catania.
During the daytime between 03:00 p.m. and 08:00 a.m. GMT the quarry
explosions can be supposed to be substantially absent. The activity during these
hours shows a quite uniform distribution over the area covered by the seismic
network, whereas the aforementioned concentration of events between Augusta and
Siracusa widely disappears (Fig. 3a). Vice-versa, in the hours from 08:00 a.m. to
03:00 p.m GMT the majority of events occurs in the coastal range between Augusta
and Siracusa, some more close to the town of Lentini (Fig. 3b). It is no surprise that
many of these events were located close to quarries, even though some of these
locations are affected by high uncertainties. A good deal of these events has
waveforms which we found to be quite typical for quarry blasts, at least at certain
stations. On the other hand we can provide a training set of signals, whose origin as
tectonic microearthquakes is out of doubt since quarries do not launch explosions
during night times.
WAVEFORMS AND SPECTRA
The superficial position of explosion sources together with the techniques of
firing during quarry explosions should produce specific signal characteristics, which
should help for discrimination purposes. In ripple firing quarries P-onsets are often
emergent, clear S-waves are lacking, whereas Raleigh-waves are observed. In Fig. 4
the waveforms and spectra of a microearthquakes and of a quarry blast both,
recorded at station SR6 are compared. We note indeed, for quarry blast, an
emergent P-onset, and a clear S (in terms of a “secondary” waves) cannot be
identified. A typical feature of explosion seismograms at this station is an envelope of
GNGTS – Atti del 19° Convegno Nazionale / 12.10
1.E-03
Ground velocity (m/s)
Ground velocity (m/s)
fish-like shape. Amplitudes of the vertical component are dominating over the
horizontal components ones.
The quarry blasts close to the city of Lentini can be distinguished rather clearly
using the station SR2, where we note a very sharp S-wave onset on all three
components of the earthquake seismogram (Fig. 5). Here peak amplitudes of the
horizontal components are about three times higher than one the vertical. For the
explosion seismogram amplitudes of the vertical and horizontal components are of
the same order. A first visual distinction between earthquakes and explosions thus
may carried out more safely using stations SR2, SR6 as key stations.
5.E-04
0.E+00
-5.E-04
-1.E-03
0
2
4
6
8
10
3.E-03
2.E-03
1.E-03
0.E+00
Z
-1.E-03
-2.E-03
-3.E-03
12
0
2
4
2.E-03
1.E-03
5.E-04
0.E+00
-5.E-04
-1.E-03
-2.E-03
-2.E-03
0
2
4
6
8
10
1.E-03
N
-1.E-03
-2.E-03
-3.E-03
0
Ground velocity (m/s)
Ground velocity (m/s)
5.E-04
0.E+00
-5.E-04
-1.E-03
-2.E-03
6
2
4
6
8
10
12
8
10
3.E-03
2.E-03
1.E-03
0.E+00
E
-1.E-03
-2.E-03
-3.E-03
12
0
2
4
Time (s)
6
8
10
12
Time (s)
1.E-02
Spectral density ground velocity (m)
1.E-03
Spectral density ground velocity (m)
12
Time (s)
1.E-03
4
10
0.E+00
12
2.E-03
2
8
2.E-03
Time (s)
0
6
Time (s)
Ground velocity (m/s)
Ground velocity (m/s)
Time (s)
1.E-04
1.E-05
1.E-06
1.E-07
1.E-03
1.E-04
1.E-05
1.E-06
0.1
1
10
Frequency (Hz)
100
0.1
1
10
100
Freque ncy (Hz)
Fig. 4 - Example of seismograms and spectra at SR6 station: earthquake (left); explosion (right). From
top to bottom: vertical (Z), north-south (N) and east-west (E) components.
The spectra analysis was carried to establish a simple criterion for the
discrimination between quarry blasts and earthquakes, which might be applied also
by an automatic discrimination. The spectra for which we show examples have been
obtained from the vertical components after applying a Butterworth band-pass filter
with corner frequencies of 1.5 and 20 Hz and two filter sections. We considered a
GNGTS – Atti del 19° Convegno Nazionale / 12.10
6.E-03
4.E-03
2.E-03
0.E+00
-2.E-03
-4.E-03
-6.E-03
-8.E-03
Ground velocity (m/s)
Ground velocity (m/s)
time window of length 8 s, starting at the P-wave arrival without taking care about
effects of mixing P and S-waves.
Despite the crude preprocessing, the differences between the spectra of quarry
explosions and earthquakes are clear. Contrary to nuclear explosions the
seismograms of quarry blasts are poorer in high frequencies than seismograms of
earthquakes with comparable magnitudes (Evernden, 1969; Elvers, 1974). The
ground velocity spectra of the earthquakes studied here show dominating
frequencies of up to 20 Hz, whereas the peaks in the spectra of the explosion
seismograms fall into ranges between 3 an 10 Hz. Dominating frequencies are
particularly low for the quarries between the towns of Augusta and Siracusa, where
they hardly exceed 5 Hz. The spectra of the blasts close to Lentini show dominating
frequencies between 5 and 10 Hz, anyway fairly below the dominating frequencies of
earthquake spectra.
0
2
4
6
8
10
2.E-03
1.E-03
Z
0.E+00
-1.E-03
-2.E-03
12
0
2
4
2.E-02
1.E-02
5.E-03
0.E+00
-5.E-03
-1.E-02
-2.E-02
-2.E-02
0
2
4
6
8
10
6
1.E-03
N
-1.E-03
-2.E-03
2
4
Ground velocity (m/s)
6
8
10
12
Time (s)
8
10
2.E-03
1.E-03
0.E+00
E
-1.E-03
-2.E-03
-3.E-03
0
12
2
4
6
8
10
12
Time (s)
Time (s)
1.E-03
1.E-03
Spectral density ground velocity (m)
Spectral density ground velocity (m)
12
0.E+00
0
Ground velocity (m/s)
4
10
2.E-03
12
2.E-02
2.E-02
1.E-02
5.E-03
0.E+00
-5.E-03
-1.E-02
-2.E-02
2
8
3.E-03
Time (s)
0
6
Time (s)
Ground velocity (m/s)
Ground velocity (m/s)
Time (s)
1.E-04
1.E-05
1.E-06
1.E-07
1.E-04
F
i
g
u
r
e
5
c
1.E-05
1.E-06
1.E-07
0.1
1
10
Freque ncy (Hz)
100
0.1
1
10
100
Freque ncy (Hz)
Fig. 5 - Example of seismograms and spectra at SR2 station: earthquake (left); explosion (right). From
top to bottom: vertical (Z), north-south (N) and east-west (E) components.
Having established sound criteria for the discrimination, we may reconsider the
distribution of the quarry blasts and earthquakes with respect to space and daytime.
GNGTS – Atti del 19° Convegno Nazionale / 12.10
Infact, as suspected earlier, the “true” seismicity is dispersed rather uniformly over
the coastal zone and inland area (Fig. 6). The cluster to the north is related to two
earthquake swarms occurring in November 1999 and January 2000 (Scarfì et al.,
2001). On the other hand quarry blasts are indeed concentrated between Augusta
and Siracusa, some more blasts were localized close to the town of Lentini. The
daytime distribution reveals, that most of the quarry explosions are launched
between 09:00 a.m. and 10:00 a.m. GMT and are restricted to the working days
Monday to Friday (Fig. 7).
AN
NI
IO
A
SE
Lentini
Siracusa
IB
LE
AN
PL
AT
FO
RM
Augusta
SICILY
C
HANN
EL
Ragusa
0
10
20
Km
Fig. 6 - Epicenter distribution of earthquakes (open circles) and quarry explosions (full dots) on the
base of location with HYPOELLIPSE. The time span considered is September 1999 to February 2000.
Note that some of the quarry blasts were localized in the sea which is probably an effect of the small
number of good onset readings available.
DISCRIMINATION OF QUARRY BLASTS FROM EARTHQUAKES WITH
ARTIFICIAL NEURAL NETWORKS AND APPLICATION OF ANN TO IBLEAN
PLATEAU DATA
In order to verify our ‘visual’ classification we have applied so called Artificial
Neural Networks (ANN) to our discrimination problem. This technique is
straightforward for this task since, contrary to conventional classification techniques,
it does not require any a priori knowledge about the mathematical structure of the
discrimination function.
The afore mentioned generality of ANNs requires at minimum a topology with
three layers of nodes, i.e., layers with input, hidden and output nodes, respectively.
The input layer serves for the storage of the data vector U, with components
normalized with respect to a maximum range between –1 to 1. This input vector U is
passed along the interconnections with the weights wij to the next, the "hidden" layer.
After applying an activation function to the values in the nodes of the hidden layer,
GNGTS – Atti del 19° Convegno Nazionale / 12.10
the values are passed to the nodes of the output layer along the interconnections
with the weights wik. In the output layer the values obtained by the network are
compared with the known ones Y. During the “training phase” the weighting
coefficients are adjusted in an iterative procedure minimizing the mismatch between
target and calculate out Y. Here we have been using the “Back Propagation
Algorithm” (see Werbos, 1974, Rummelhart et al., 1986). Finally we obtain a function,
which maps the input vector U to the output vector Y:
NH
yˆ k ( U)   c j ( wTj  U  t j )  c0
j 1
where:
ŷ k = k-th element of Y estimated by the network; U = input vector; wj, = vectors of
weights between input and hidden layer; cj = weights between hidden and output
layer; tj = biases.; () = sigmoid activation function (z)=1/(1+e-z)
20
a)
)
18
NUMBER OF BLASTS
16
14
12
10
8
6
4
2
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23
HOUR (GMT)
14
12
b)
NUMBER OF BLASTS
10
8
6
4
2
0
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Fig. 7 - Daily (a) and weekly (b) distribution of quarry blasts recorded in the time span September
1999 - February 2000.
GNGTS – Atti del 19° Convegno Nazionale / 12.10
A suitable transformation of the data facilitates the task of signal classification
with ANN. As Falsaperla et al. (1996) and Langer et al. (1996) point out, the use of
autocorrelation functions instead of plane waveforms improves the results, probably
because no problems with phase alignment occur. Besides this, autocorrelation
functions represent practically the same information as do amplitude spectra, which
have proven to provide significant criteria during the ‘visual’ classification.
Autocorrelation functions are normalized by definition, therefore, they can be directly
used as input vectors in ANN applications. In data preprocessing autocorrelation
functions have been preferred to plane waveform and spectra.
1
a)
Score Mismatch [dl]
0.5
0
-0.5
-1
1
b)
Score Mismatch [dl]
0.5
0
-0.5
-1
Fig. 8 - Classification mismatch of test set data at stations SR2 (a) and SR6 (b). The time span
considered is September 1999-February 2000.
We applied the ANN to the data recorded at the key stations SR2 and SR6 in
the time interval from September 1999 to February 2000. The autocorrelation
functions were obtained from the filtered traces. We divided the data set into two
almost equal parts for the training and test data set, choosing the test set events
randomly 61 events made up the test set of station SR2 and 60 events at station
SR6. We used a length of the input vector of 100, i.e., the first 100 points of the
GNGTS – Atti del 19° Convegno Nazionale / 12.10
autocorrelation functions. The output vector is made up by two elements. Thus, the
desired output vector Y for an earthquake reads as (1,0), conversely (0,1) for a
quarry blast. A classification of a test set event is considered as “failed” if the
difference between calculated and desired output of either of the two elements of Y is
larger than 0.5. After some trial and error experiments we adopted an ANN with 5
hidden nodes (together with 100 nodes in the input layer and 2 nodes in the output
layer). The results of the classification are showed in Fig. 8 where we note two
misclassified signals (earthquakes) at station SR2, and one misclassified signal
(quarry blast) at station SR6.
DISCUSSION AND CONCLUSION
The presence of quarries in an active seismic zone may cause severe
misunderstandings concerning the distribution of microseismicity. In our particular
case an uncritical representation of event location makes believe that the coastal
zone between Augusta and Siracusa has an enhanced level of activity compared to
the inland areas of the Iblean Plateau. However, the epicenters of events occurring
during night times are quite uniformly distributed over the area covered by the
seismic network. On the other hand, the events occurring at critical hours, i.e.,
between 09:00 a.m. and 10:00 a.m. GMT are concentrated in a dense cloud of
epicenters between Augusta and Siracusa.
As it turns out difficult to distinguish between quarry blasts and tectonic
earthquakes on the base of hypocenter locations we have been analyzing waveforms
and spectra defining some kind of training set by taking only events occurring during
night times. These events represent true tectonic earthquakes, since quarries launch
explosions only during working hours. The visual distinction between waveforms of
earthquakes and of quarry blasts, particularly when recorded on paper drum
recorders, is not evident at all stations. Nevertheless, it was possible to identify two
key stations (SR2 and SR6) where the differences between digital waveforms of
earthquakes and of quarry blasts appear rather clear.
The differences between earthquakes and quarry blasts turned out most clearly
from their spectra. Even reducing the signal preprocessing to a level which could be
reached also by an automatic treatment, we find dominating frequencies of
earthquakes being significantly higher than those of quarry blasts. This is observed
also at stations where the visual inspection of waveforms led to some uncertainty of
the correct event classification.
As a ‘visual’ discrimination can always be argued as arbitrary, we have applied
artificial neural networks in a supervised classification of signals as tectonic
earthquakes or quarry explosions. This means that we defined training sets of
events, whose origin (tectonic or artificial) was supposed to be known, and estimated
a discrimination function, which can be arbitrarily complex. The performance of this
function was checked by applying it to a set of test events, again with known origin,
but not used for establishing the discrimination function. Given the high rate of
success, on average about 95%, we conclude that our ‘visual’ classification first
carried out is mathematically reproducible and thus far from arbitrary. As the
preprocessing steps are simple, it is possible use ANN as a reliable tool for an
automatic discrimination of quarry blasts from local tectonic microearthquakes.
GNGTS – Atti del 19° Convegno Nazionale / 12.10
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