seismic source classification in israel by signal imaging ......seismic source classification in...

Bulletin of the Seismological Society of America, 89, 4, pp. 960-969, August 1999

Seismic Source Classification in Israel by Signal Imaging

and Rule-Based Coincidence Evaluation

b y G. L e o n a r d , M. Villagr~in, M. Joswig , Y. Bar ta l , N. Rab inowi t z , and A. S a y a

Abstract We tested the applicability o f the sonogram detector and a rule-based evaluation for seismic source classification and identification in Israel. Source identification is based on the transformation of full seismograms to images and the correlation between a new image and known images f rom different seismic sources. These methods were initially developed to monitor the induced seismicity of north- west Germany by the BUG small array. Two new elements were introduced in this study: (a) the adaptation of the array technique to the identification of seismic sources using a local network and (b) the recognition of source regions with natural seismicity. The tested data include 41 local earthquakes and explosions in the time period September 1997 to December 1997. Automated processing yielded 95% success in correctly identifying the individual sources with no misclassification. One event was not classified. Path and distance effects did not influence the classification capability. The method could be useful for seismic verification under the Comprehensive Test Ban Treaty.

Introduction

Kim et al. (1997) demonstrated that for events in the passband above 10 Hz the spectral content of P and S signals shows distinctly different patterns for earthquakes and explosions at local and near-regional distances and concluded that Pg/Lg spectral ratios show great promise as a reliable discriminant. They indicated that a key to the portability of this discriminant to various parts of the world is the correction of regional signals for their source receiver path effects.

Shapira et aL (1996) and Walter et al. (1997) conducted discrimination studies on seismic events from northern Israel recorded by the Israeli Seismic Network (ISN). Shapira et

al. (1996) looked at P/S amplitude ratios in the 1 to 10-Hz band with mixed results and suggested a strong dependence on path effects in this heterogeneous region. Walter et aL

(1997) tested a higher frequency (>4 Hz) regional P/S discriminant. Their results were not conclusive.

In this study we focused on the application of an alternative approach to the classification problem. This approach is based on the transformation of full seismograms to images and the correlation between a new image and known images from master events from different seismic sources. We use this master event technique to identify the following:

• Aftershocks in an earthquake sequence where the main shock can be used as the master event

• Mining explosions, given that the mine extends over the order of a few kilometers and that the mining practices are consistent over time

• Earthquakes occurring in a circumscribed area with similar focal depth

Master event techniques can provide support for the seismic monitoring efforts of the Comprehensive Nuclear Test Ban Treaty (CTBT). A seismic event that is detected by the In- ternational Monitoring System (IMS) may lead a signatory state to request a further clarification and investigation into the character of the event in the form of an On Site Inspec- tion (OSI). An inspection team is eligible to enter signatory states suspected of violating the treaty to search for traces of a nuclear test. The search begins with the rapid deploy- ment of seismic stations to look for evidence of nuclear-test- related aftershocks and to narrow the search area prior to applying other OSI technologies. The use of master events can be helpful, for example, to identify aftershocks if a state were accused of hiding a nuclear test in an earthquake swarm. They can also aid the OSI team to focus on aftershocks associated with a nuclear explosion and to differen- tiate them from other ongoing microseismicity in the region, including mining and quarry blasts.

In recent years various artificial intelligence (AI) approaches have been suggested for seismic event detection, classification, and identification (Liu and Fu, 1983; Chia- ruttini et aL, 1989; Dowla et al., 1990; Dysart and Pulli, 1990). Techniques used range from syntactic pattern recognition and expert systems to self-learning approaches such as artificial neural networks. An alternative has been to use the sonogram detector (Joswig, 1990) based on pictorial pat-

960

Seismic Source Classification in Israel by Signal Imaging and Rule-Based Coincidence Evaluation 961

tern recognition (Joswig, 1994). This single-trace detector is 32 the heart of an automated processing scheme used to classify seismic source regions by similarities between the full seis- 3~- mograms.

Subsequently, the pattern recognition approach was ex- tended to a complete evaluation system suited for small arrays with three to six stations (Joswig, 1993a). The enhanced version of the sonogram detector (SONODET) is followed by rule-based coincidence voting in a module called COASSEIN 34-- (COincidence Association in Sparse SEismic Networks [Joswig, 1995]); initial identifications are postprocessed by nonlinear correlation (Joswig and Schulte-Theis, 1994) and a three-component phase picker (Klumpen and Joswig, 1993) to yield accurate epicenter locations of mining events. All these modules were developed to classify seismic 32- sources in the vicinity of the Bochum region in Germany recorded by the BUG small array. The local seismicity in this area is dominated by rockbursts induced by coal mining and some quarrying.

The focus of this study was the adaptation of the array technique to the identification of seismic sources of low 3 0 - m a g n i t u d e using a local network. The application was restricted to the Bochum modules of the first two units, SONODET and COASSEIN.

Over the last 15 years, the Israeli Seismic Network (ISN) operated by the Geophysical Institute of Israel (GII), has monitored seismicity in and around Israel. Each detected

28-- event is telemetered to the seismological center at the GII, digitized, and processed. Location and identification of events is performed manually by analysts. In addition, con- 32 tinuous data from EIL, a broadband auxiliary station of the future IMS is telemetered to both the Israeli National Data Center (NDC) at the Soreq Nuclear Research Center and the seismological center at the GII. Recently, some of the ISN stations were upgraded so that the signals are digitized on site. Data obtained from the upgraded ISN stations and ElL formed the basis for this study.

In the following sections we briefly describe the seismic activity in Israel. We discuss the selection of the data set, introduce the essence of the pattern recognition technique and the rule based evaluation, and describe the way they were implemented for the classification of local seismic sources in the southern part of Israel as a potential model for monitoring seismic activity in the CTBT context.

The Data Set

The major earthquake sources in Israel are the Gulf of Eilat, the Dead Sea transform fault zone, and the Carmel Fa'ra fault (Fig. 1). Most of the current seismic activity from these sources has magnitude (ML) less than 3 and occurs at focal depths of 5 to 25 km. The seismicity exhibits a pro- nounced clustering in the Dead Sea fault zone and the Car- mel Fa'ra fault (van Eck and Hofstetter, 1989; Hofstetter et al., 1996). Joint focal mechanism analyses carried out on several groups of events along the Dead Sea fault system

34 36

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Figure 1. Map of Israel and surroundings showing the main sources of seismic activity.

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--34

- -32

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indicate a north-south (left lateral) strike-slip faulting mechanism (van Eck and Hofstetter, 1990). Historical seismicity gives evidence that medium to large earthquakes occurred in this region. The largest estimated earthquake (M s = 7.6) occurred on 20 May 1202 (Ambraseys and Melville, 1988). Recent events are the Ms 6.2 event on 11 July 1927, which occurred north of the Dead Sea, and the Ms 7.1 on 22 No- vember 1995 located in the Gulf of Eilat.

Natural seismicity comprises a small fraction of the events recorded by the ISN (several thousands per year). Most of the events are explosions. Therefore, the main effort of the analyst is devoted to the routine discrimination between various event types in order to extract the minority that comes from earthquake sources. Earthquakes are re- ported in the yearly bulletin of the GII. The bulletin forms the basis for seismic hazard maps.

Figure 2 shows the seismic stations and sources used in this study. The stations are part of the new digital seismic network that was recently installed in israel. At the short- period stations (JVI, YTIR, MZDA, MKT, ATR, and PRNI) the signals are digitized on site by a 21-bit (50 samples/sec)

962 G. Leonard, M. VillagT~n, M. Joswig, Y. Bartal, N. Rabinowitz, and A. Saya

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Figure 2. Location of the subnetwork stations and seismic events used in this study.

A/D converter. The data from all stations are telemetered via satellite to the GII. At the broad band station (EIL) signals are digitized on site by a 24-bit (40 samples/sec) A/D converter. These data are telemetered via satellite communica- tion to the Israeli NDC.

The data set selected for this study had to fulfill the following requirements:

• Earthquakes and quarry explosion sources within a restricted area

• The number of earthquakes approximately matching those of quarry explosions

• Each event recorded by at least three stations • Confirmed quarry blasts

The limited available data from the new network and these requirements restricted our data set selection to a 4-month period. Well-clustered seismic events provided a good data- base for the test case (Fig. 2). The tested data included 41 local seismic events from two earthquake sources, the Dead Sea and the Gulf of Eilat, and two quarry sources, in the Arad and Zin areas, recorded in the time period September- December 1997 (Table 1). All events are in the magnitude range 1 < M L < 4. The typical estimated uncertainty in the

epicenter determination is 3 km for Arad, Zin, and the Dead Sea events. The typical accuracy for Eilat earthquakes is worse (7 km) because of the unfavorable configuration of the ISN with respect to this source of seismic activity. Only vertical component data were used in the analysis.

An example of the spectral character of all seismic sources, derived from waveforms recorded at station EIL, is illustrated in Figure 3. Both earthquake sources show simi- laxity in their spectra. The similarity in the spectral shape of the two quarry sources is also demonstrated. Blasts and earthquakes show some variations in their spectral character. The excitation of surface waves is clear on seismograms from blast sources.

The Selection of Master Events

Experienced analysts accustomed to looking at raw data remain one of the most reliable sources of knowledge for screening events. In order to reproduce this knowledge we applied the sonogram approach to raw data. Sonograms are the graphic representation of spectral analysis on consecu- tive time windows along a time series. Master time series, regarded as typical for specific sources, are selected. These time series are transformed into sonograms. In the transformation process the signals are divided into windows of equal length and spectrally analyzed. The spectral range is divided into 11 logarithmically spaced bins, and the average spectral density, over such a bin, corrected for noise offset, is stored in a sonogram matrix of frequency bin versus time window. The transformation (Joswig, 1990, 1995) is universal enough to produce images with constant scaling from every kind of seismogram. This detector weights the fit between a selected pattem P+(/5,t) and the denoised sonogram D + ~ t ) by

fit('O =

D + ~ t + z)P + 05, t) f,t

0.5 ( ~ D+ 05, t + ~)2 + ~ p + ~ t)2) f,t f t

For each seismic source a single pattern is stored in the computer memory as a reference pattern in a 2D image of signal energy in time and frequency. For each seismic station the selected patterns of all sources are stored. The selected patterns depend on the source signature and the ray path. Patterns from the same source differ from one seismic station to another, since they travel along different paths. Thus, an independent bank of master events was formed for each station. Figure 4 shows the selected master patterns of all seismic sources stored at station PRNI. The variation in the spectral content as illustrated in the sonograms provides an important insight into the sources' identification capability. Two interrelated criteria are required for representing a cluster as a master event: (a) the signal's amplitude should be relatively high and (b) the signal to noise ratio (SNR) should be relatively high.

Seismic Source Classification in Israel by Signal Imaging and Rule-Based Coincidence Evaluation

Table 1 Events Used in This Study

963

Year Month Day Hr Min Sec Lat Lon Dep ML ~pe*

1997 09 01 09 26 19.6 31.113 35.123 0.0 2.3 E-ARAD 1997 09 02 05 25 41.6 29.304 34.669 0.0 2.0 Q-EILAT 1997 09 02 09 03 54.7 30.857 35.004 0.0 2.1 E-ZIN 1997 09 14 08 28 09.8 31.121 35.179 0.0 3.0 E-ARAD 1997 09 16 10 17 51.1 30.828 35.054 0.0 2.4 E-ZIN 1997 09 17 10 52 33.7 30.832 35.065 0.0 2.4 E-ZIN 1997 09 21 14 53 18.2 31.464 35.427 15.0 2.1 Q-DEAD SEA 1997 09 22 22 49 52.4 31.461 35.407 9.0 1.6 Q-DEAD SEA 1997 09 23 01 54 35.7 31.461 35.437 15.0 1.8 Q-DEAD SEA 1997 09 23 02 27 24.4 31.462 35.413 13.0 2.0 Q-DEAD SEA 1997 09 23 15 23 54.2 31.456 35.445 12.0 2.6 Q-DEAD SEA 1997 09 25 16 38 00.4 29.304 34.824 5.0 3.5 Q-EILAT 1997 09 26 07 6 29.4 31.445 35.502 0.0 2.5 Q-DEAD SEA 1997 10 02 00 45 36.9 31.443 35.487 11.0 2.5 Q-DEAD SEA 1997 10 02 01 28 44.2 31.454 35.431 15.0 1.8 Q-DEAD SEA 1997 10 04 14 6 33.8 29.323 34.848 5.0 2.2 Q-EILAT 1997 10 06 07 22 38.0 29.359 34.713 0.0 3.1 Q-EILAT 1997 10 05 09 42 42.1 31.091 35.181 0.0 2.4 E-ARAD 1997 10 07 08 56 53.4 31.082 35.147 0.0 2.4 E-ARAD 1997 10 12 09 28 15.0 30.870 35.067 0.0 2.4 E-ZIN 1997 10 13 09 04 40.7 30.803 35.042 0.0 2.0 E-ZIN 1997 10 13 09 38 48.8 30.822 35. 029 0.0 2.8 E-ZIN 1997 10 14 00 13 55.7 31.461 35.424 14.0 1.8 Q-DEAD SEA 1997 10 14 09 25 55.8 30.872 35.094 0.0 2.4 E-ZIN 1997 10 23 16 28 51.7 29.371 34.808 0.0 2.0 Q-EILAT 1997 10 26 09 41 48.5 30.801 35.020 0.0 1.9 E-ZIN 1997 10 28 09 23 00.3 31.096 35.185 0.0 2.6 E-ARAD 1997 10 31 20 24 08.2 29.433 34.930 10.0 1.8 Q-EILAT 1997 11 06 09 51 02.6 30.792 35.113 0.0 2.3 E-ZIN 1997 11 09 09 03 33.8 30.844 35.093 0.0 2.2 E-ZIN 1997 11 12 09 37 50.7 31.086 35.206 0.0 2.1 E-ARAD 1997 11 17 10 14 30.7 31.100 35.177 0.0 2.8 E-ARAD 1997 11 18 09 45 31.8 30.811 35.039 0.0 2.3 E-ZIN 1997 11 25 03 56 08.5 31.472 35.438 15.0 1.8 Q-DEAD SEA 1997 11 26 10 24 17.1 30.804 35.022 0.0 2.2 E-ZIN 1997 12 04 09 36 26.1 31.118 35.163 0.0 2.8 E-ARAD 1997 12 19 13 28 33.0 29.431 34.822 4.0 2.3 Q-ElLAT 1997 12 19 15 22 26.0 29.449 34.853 4.0 2.0 Q-EILAT 1997 12 23 10 00 43.2 31.141 35.178 0.0 3.0 E-ARAD 1997 12 29 10 13 17.0 31.113 35.177 0.0 2.8 E-ARAD 1997 12 30 09 34 41.8 31.103 35.195 0.0 2.2 E-ARAD

*Q, earthquake; E, explosion.

Source Identification by Single Stations

T h e S o n o g r a m D e t e c t o r

T h e iden t i f i ca t ion p r o c e d u r e t r ans fo rms each n e w re-

co rded s igna l a t e a c h s ta t ion in to a s o n o g r a m and c o m p a r e s

it to t hose a l ready s tored as m a s t e r events . In o the r words ,

the m a t r i x o f the n e w tes ted s igna l is c ross -co r re l a t ed w i th

each o f the s tored m a s t e r e v e n t mat r ices . Thus , the s o n o g r a m

de tec to r c o m p a r e s ac tua l even t s w i th p rede f ined pa t t e rns and

ra tes t he resu l t s in r e c o g n i t i o n ca tegor ies b a s e d o n leve l s o f

r e s e m b l a n c e . T h e r ecogn i t i on ca t ego ry is d e t e r m i n e d b y two

values : (a) the va lue o f the fit, w h i c h is the m a x i m u m am-

p l i tude o f the c ross -co r re l a t ion func t ion , and (b) the per-

c e n t a g e o f the ac tual m a s t e r m a t r i x con t r i bu t i ng to the cross-

co r re l a t ion func t i on va lues a b o v e a ce r ta in th reshold . The i r

a m o u n t wi l l va ry d e p e n d i n g on the ac tual SNR.

T h e pa t t e rn fit is j u s t l ike any c ross -cor re la t ion func t ion ,

w i th va lues n o r m a l i z e d in the r a n g e o f [ - 1 , + 1], w h e r e

fit = 0 m e a n s tha t the two p a t t e m s are unco r r e l a t ed and

fit = 1 m e a n s tha t the two pa t t e rns are ful ly corre la ted . T h e

r ecogn i t i on ca tegor ies are def ined as fo l lows:

• Clear, fit > 0.9 and p e r c e n t a g e > 80%

• Probable, 0.6 < fit < 0.9 and p e r c e n t a g e > 6 0 %

• Possible, 0.4 < fit < 0 .6 and p e r c e n t a g e > 4 0 %

• No detection, fit < 0.4, the pa t t e rn fit does no t exceed the

t h r e sho ld va lue

T h e h ighes t t h re sho ld su rpassed d e t e r m i n e s the f inal

964 G. Leonard, M. Villagr~in, M. Joswig, Y. Bartal, N. Rabinowitz, and A. Saya

(D 13

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P spectrum for source "Arad"

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P spectrum for source "Zin"

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spectrum for source "Zin"

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P spectrum for source "Dead Sea" 1

2 4 6 B 10 12 14 16 18 20 2 4 6 8 10 t2 14 16 18

Frequency (Hz] Frequency (Hz}

Figure 3. Uncorrected P-wave and S-wave spectra for two explosions in the Arad and Zin quarry areas (top) and for two earthquakes from the Dead Sea and Gulf of Eilat clusters (bottom).

identification. For probable and possible recognition categories a second choice (2nd guess) for the identification is allowed. This is used in cases where the tested pattern re- sembles two master patterns from two different sources.

For the Arad and Zin quarry explosions the events recorded on 14 September (08 hr 28 min) and 13 October (09 hr 38 min), respectively, were used as master events. For Dead Sea and Eilat earthquakes the events recorded on 23

September (15 hr 23 min) and 25 September (16 hr 38 min), respectively, were used as master events.

Test Results

Table 2 presents the classification performance of all stations. The determination of success or failure was in com- parison to the GII seismological division catalog. The performance is presented in three categories: correct source


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Figure 4. Seismogram traces and associated sonograms from four source regions stored as master events for station PRNI, Spectral power over each spectral bin (y axis) with a time window of 1.5 sec duration (x axis) is expressed by a color scale.

966 G. Leonard, M. Villagr~.n, M. Joswig, Y. Bartal, N. Rabinowitz, and A. Saya

Table 2 Classification Results (in Number of Events) at Single Stations

ATR EIL JVI MKT MZDA PRNI YTIR

Explosions Correct source 12 9 6 8 17 16 17 Misclassification 3 3 4 5 1 6 0 No identification 1

Earthquakes Correct source 15 9 11 10 11 12 12 Misclassification 2 1 0 0 0 5 1

Number of evaluated traces = 197.

identification, misclassified events, and no identification. In the following, the discrimination performance of single stations is summarized. Herein, we present the performance of the first and second category. Correct source identification results are 84% for ATR, 78% for EIL, 81% for JVI, 78% for MKT, 97% for MZDA, 72% for PRNI, and 97% YTIR. Mis- classification results are 3% for ATR, 9% for EIL, 5% JVI, 0% for MKT, 3% for MZDA, 3% for PRNI, and 3% for YTIR. Of 108 single traces from explosion sources, 85 correctly identified the specific source. Of 89 single traces from earthquake sources, 80 correctly identified the specific source.

We also tested the sensitivity of the classification label (Clear, Probable, or Possible) to SNR (Table 3). For this evaluation we selected three stations, EIL, PRNI, and MZDA, and two proximate clusters, the Dead Sea earthquakes and Arad quarry explosions. EIL and PRNI are located along the same path to both sources at varying distances. In contrast the path from both clusters to the closest station, MZDA, is different with a similar distance range. Figure 5 shows waveforms and associated sonograms for one of the Dead Sea earthquakes recorded at the three stations. Note the changes in the trace lengths, level of energy, and spectral content as a function of distance. All earthquakes were correctly identified by all stations. One explosion was misclassified as a quake by one station (PRNI). There is a correlation between the classification label to SNR. However, there is no ob- served effect on the final classification decision at the single stations. Path and distance effects do not influence the classification capability.

Source Identification by a Network

Rule-Based Coincidence Evaluation

The principal idea of this approach is the integration of the single-station identifications into a network classification. The integration is carried out by a set of rules. Four fundamental types of rules are used: (a) network event (NE) creation rule, (b) selection rules, (c) resolution rules, (d) reevaluation rules. The processing module, COASSEIN, de- scribed in detail by Joswig (1995), is used. In this study we

Table 3 Relation between Classification Labels and Signal-to-Noise

Ratio at Single Stations

Classification MZDA PRNI EIL Label [19] [20] [10]

Clear 32% 20% 10% (28) (24) (6)

Probable 42% 55% 0% (23) (19)

Possible 16% 20% 90% (17) (12) (4)

Wrong 0% 5% 0% (15)

Number of evaluated traces is in brackets. Rate with respective classification labels. In parentheses of SNR (average dB).

(%) of traces in stations are the associated values

limited COASSEIN to handle two types of sources based only on the first three rules.

In the simple case where all station messages agreed, a final unique NE identification is derived. However, in gen- eral, the station messages differed and resolution rules were applied to derive a network identification. Here we demon- strate the essence of applying the rules through two exam- pies. The first is based on an event from 13 October 1997 at 09 hr 04 min (Table 1). The event was recorded by three stations, JVI, MKT, and PRNI. Each station gave one decision in the sonogram message list as follows: JVI, possible Dead Sea quake; MKT, possible Eilat quake; PRNI, possible Zin quarry explosion. All the sonogram messages were consid- ered as station events (SE). The first rule is the NE-Creation Rule; an NE is formed by combining all SEs with one identification per station only. In this example, the three stations yield three SEs, which form 1 NE. Each SE has a cost pa- rameter that encodes the recognition category: clear, cost = 0; probable, cost = 20; possible, cost = 40; no detection, cost = 0.

The NEs cost is generated by the summation of its SEs costs. The selection rules try to find in the NE list an item


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Figure 5. Waveforms and associated sonograms for one of the Dead Sea earthquakes recorded at stations MZDA, PRNI, and EIL (from left to right). Note the changes in the trace length, energy level, and spectral content as a function of distance.

with the lowest cost and without contradiction in its single- traces event types. In this example, all SEs messages contra- dict each other and no final unique NE identification is derived. Thus, no decision is made regarding the source identification.

The second example highlights the way a network decision is executed based on an event from 23 September 1997 at 01 hr 54 rain (Table 1). The event was registered at six stations: ATR, JVI, MKT, MZDA, PRNI, and YTIR (Figure 6). The decisions in the sonogram message are as follows:

ATR, probable Dead Sea quake ATR, 2nd guess Arad quarry explosion JVI, probable Dead Sea quake MKT, possible Dead Sea quake MKT, 2nd guess, Eilat quake MZDA, probable Dead Sea quake PRNI, probable Dead Sea quake YTIR, clear Dead Sea quake

As noted previously, for probable and possible categories a second guess is allowed by the algorithm. Therefore, a recorded signal may give a double identification, and the station thus contributes 2 SEs to the SE list. In this case two stations (ATR and MKT) give a multiple identification. Thus, the number of identifications is larger than the number of stations, leading the NE creation rule to form more than one NE out of the SE list. In this example, the 6 stations yield 8 SEs, which form 4 NEs. Again the NE' s cost is generated by the summation of its SE's costs (0 for clear, 20 for probable, and 40 for possible and 2nd guess), In this example the event type--Dead Sea quake--is taken as the final conclusion with the lowest given cost (120) and without contradiction in its

single-traces event types. The quality of coincidence is as- signed by three ranks high (***), medium (**), and low (*).

Test Results

The network performance is presented in two categories: correct source identification and rnisclassified events. Seventeen out of 18 earthquakes were correctly identified (12 with the highest rank, 2 with medium rank, and 4 with the lowest rank), and in one case the source was misidenti- fled (Dead Sea quake instead of Eilat quake). Not a single earthquake was misclassified as a blast, yielding a 0% misclassification percentage. Twenty-two out of 23 explosions were correctly identified (10 with the highest rank, 8 with a medium rank, and 4 with the lowest rank), and in one case no conclusion was reached. Therefore the misclassification percentage is 0%. Thus, the performance of the integrated network evaluation by COASSEIN based on single-station detection by the SONOGRAM detector can be ranked as very good.

Discussion and Conclusions

This study was aimed at testing the effectiveness of a specific master event technique, the SONOGRAM detector, as an automatic method for identifying seismic events during the stage of an OSI in the framework of the CTBT. This technique is based on the following initial steps: (a) prede- termination of seismic sources and (b) selection of events as master patterns, which are transformed into sonograms and stored at each seismic station

Recorded signals are transformed into sonograms and correlated to the master patterns. Results are obtained based on the degree of resemblance between the new signals and

968 G. Leonard, M. Villagnln, M. Joswig, Y. Bartal, N. Rabinowitz, and A. Saya

ATR S Z

Plotstarttime:1997 923 1:54 21.355 Filt: 1.000 5.000

M SZ

MKT S Z

PRNI S Z

MZDA S Z

YTIR S Z

m7

40 60 20 40 60 20 40

Figure 6. Traces recorded at stations ATR, JVI, MKT, MZDA, PRNI, and YT1R from an event that occurred on 23 September 1997 at 01 hr 54 min (Table 2).

those already stored as master patterns. The results obtained at individual stations are integrated into the final classification. For the subnetwork evaluation we adapted a rule-based routine (COASSEIN), which has the ability to imitate the human decision-making process of reasoning and selection.

The test included 42 local events from four clusters recorded by seven seismographic stations. The performance of SONODET and COASSEIN compares favorably with the G2I seismological division catalog. The system results for these events yielded 95% success in correctly identifying the individual sources, with no misclassification. In one case no conclusion was made. The results correspond well to those achieved with the BUG small array (Joswig, 2995), indicat- ing that the chosen approach is suitable for the discrimination of earthquakes from quarry explosions at local distances

using mixed networks (integration of arrays and single stations).

The use of sonograms for source classification by pattern recognition utilizes the signature of the whole seismogram including path effects, so its discrimination is based on a mix of source and path properties, effectively representing characteristics of specific source regions. Therefore the robustness of the method is not limited to specific pass- bands for low-magnitude events, and its applicability does not require correction of regional signals for their source- receiver path effects. This approach is more specific than identification based solely on parameterization of source effects and more robust in a noisy situation; thus, it is appli- cable to most routine situations with known explosion sites (Hedlin et aL, 2990; Wuester, 2993).


It is clear at this stage that the system may be incorpo- rated as an effective tool to assist human analysts in their routine work in cases of monitoring aftershocks in an earthquake sequence, mining events (given that the mine extends on the order of a few kilometers), and natural seismic events occurring in a circumscribed area with little variation of the depth.

The SONOGRAM detector also has a few advantages in the analysis of data from the portable network deployed during the reconnaissance stage of an OSI. It carl serve as an intelligent remote logger with near-real-time automated decision and evaluation capability of detected events based on continuous data, minimizing the transfer of data to the central processing site. The events detected and recognized at the remote site can be transmitted to the central processing site for a network decision based on COASSEIN.

The event detection algorithm is capable of recognizing seismic events within the background noise (Joswig, 1993b) and automatically identifying unusual events. This is as- sisted by interactive visualization of the patterns of the source characteristics. SONODET and COASSEIN could be integrated to both Cooperating National Facilities (CNF) stations and the portable network as an additional tool for seismic verification.

Acknowledgments

This study was funded by the Israeli Atomic Energy Commission. We thank T. Boutbul and Z. Somer for their contribution, and Y. Weiler, S. Lewis, and D. M. Steinberg for the review of the manuscript. Software modules SONODET and COASSEIN are available as part of the IASPEI share- ware library of seismological software. They can also be accessed at anon- ymous ftp of ndc.soreq.gov.il/publmanfred/SONODET

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Israel Atomic Energy Commission Box 7061 Tel Aviv, Israel

(G. L.)

National Data Center Soreq, Nuclear Research Center Yavne 81800, Israel

(M. V., M. J., Y. B., A. S.)

Institute of Solid Earth Physics University of Bergen Allegaten 4t, 5007 Norway

(M. V.)

Geophysical Institute of Israel Box 2286 Hoton, Israel

(N. R)

Manuscript received 3 January 1995.

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