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Title: Earth Sciences/Geophysics/Earthquakes - Automatic monitoring of regional seismic events Technical paper describing an approach to automatic, real-time monitoring of regional seismic events, based on the theory of pulse propagation in a randomly stratified medium with waveguides.

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Automatic monitoring of regional seismic events IRIS Newsletter, vol. XV, "No. 1. Spring 1996", pp. 12-14 + extra figures/comments

A novel approach to automaticmonitoringof regional seismic events.Gennady A. Ryzhikov, MarinaS. Biryulina,and Eystein S.HusebyeUniversity of Bergen, NorwaySummaryImproved event detection and location capability of regionalnetworks can be achieved by developing and incorporating new concepts forseismic data analysis. Our strategy for automatic event location is tiedto transforming high-frequency data to energetic waveletenvelopes (EW-transform) and isanchored in the theory of pulse propagation in a randomly stratified mediumwith waveguides. Testing the new method on mining events from southernNorway, our epicenter determinations were far better than those derivedby the analyst (bulletins). In Germany our scheme could handle very weakevents for which interactive analysis failed. With the method it is possibleto reduce the data volume for on-line transmission - by in situ (i.e.at the recording site) resampling of records from digitizing frequency40 - 80 Hz to 2 Hz. Our automatic location scheme is 'robust' in the sensethat no crustal information is needed for its realization, once the networkhas been trained through the development of proper EW travel timecurves.Event location/detectionThe conventional approach to the problem of seismic event detectionand subsequent localization is a four-step process:(1) signal detection,(2) phase identification (P, S, etc),(3) phase association (matching phases from many stations),and(4) event location using 'phase association' parameters.This approach is not attractive for automated locationanalysis; a four-step process is rather clumsy and for poor to moderatesignal-to-noise ratio the first 3 tasks are error-prone.We find that by using the energetic wavelet envelope transform(EW-transform) of records, we can mergethe above 4 tasks into one; that is reformulate the problem as ajoint event location/detection problem.The steps involved are: EW-association -> eventpre-location -> EW-identification ->eventdetection. These steps in our real-time event localization algorithmaredescribed below.In-situ seismic record analysis.The 'raw' vertical-component high-frequency records are pre-filtered in the band 2-4 Hz and/or 5-10 Hz, where the signal-to-noiseratio is optimum for local/regional events, and then subjected tothe EW-transform as illustrated in Figure 1and 2. P-, and S-Wavelet kinematics: Italy

Fig. 1. Example of energetic wavelet processinga)from stations (greencircles) of theItalian network for an event 26.02.1995 {red rhombus}.b).Original waveform records and EW-envelopes. First arrivals are markedwith flagsc) The entire set of relevantdimensionlessenvelopes, ordered with respect to epicenter distances.Amplitude isolines are drawn below. Note the linear spreading ofEW( c) ) which is typical of diffusion processes.d)P- andS-energetic wavelet travel times curves for Italian, Norwegianand German networks. The P- and S-EWmaxima are automatically identified and picked at the event post-localizationstage. The dispersion of maxima for the German network was essentiallyreduced after a brief period of 'network training'. The corresponding EW-velocitiesare 6.3 km/s and 3.5 km/s for Italy/Norway and 5.9 km/s and3.4 km/s for Germany.The theoretical basis of EW-transform is thatpulse propagation in a randomly stratified medium should create anenergy wave train with diffusion in space and time, and therefore the energydistribution recorded by a station can be interpreted as a randomrealization of a diffusion process in time domain. Two main wavefield intensitycomponents occur in the vicinity of the free surface, namely primaryenergetic wavelets, or P- EW,and secondary-, or S-EW,which exhibit distinct group velocities which are quite different from Pn, Sn or Lg phase velocities . It is important to note that these velocities are nearly independentof local crustal structure, focal depths and source mechanisms (Figure1) ,as expected from theory.The validity of this EW-transform was tested on real data fromGermany, Italy and Norway and the results are presented in Figure 1d. Similarphenomena would exist in a deterministic isotropic stratified medium withwaveguides. [Kennett, 1983]. It is sufficientto transmit just EW-transformed traces to the network HUB for subsequentevent location and detection analysis.Event location.We pose the problem as a linear inversion of EW- forms with respect to an artificialenergeticsource image: i.e. an arbitrary space/time distributionof point-like incoherent sources . An infinite set of distributionsexists that can fit the observed data quite well, but there shouldbe just one that approximates an impulse emitted at a proper time/spacelocation. Note, that a network area is defined by a minimumof 4-5 network stations - which are located at distances from the sourceof less than 1000 km - in our tests we used grid size 10 x 10 deg2 in latitude/longitude and gridding units 20 km and 1 sec in space/time.Anessential step in network training is estimation of P-and S- EW velocities, or a part of self-learning ofnetworks. This involves joint inversion of EW-forms from N events withrespect to 3xN parameters (epicenter coordinates, origin times) plusproper P- and S- EW velocities, from which travel time curves are constructed.The steps involved in the location procedure are:Normalized migration:each network station is considered to be a source whichin reversed time 'emits' all samples of the corresponding EW-recordinto the network area with appropriate P- and S-EW velocities.[Note, to avoid errors during estimation of a 'true' amplitude, allEW-records are normalized with respect to the corresponding maxima].This procedure provides us with a source image in the networkmonitoring area at each 0.5 - 1.0 sec (depends on a digitizing frequencyof EW-records).The normalized migration applied here is similar to thatdescribed by Biryulina and Ryzhikov [Ryzhikov and Biryulina,1995]Source image dimensionless measure:We extract the best source image 'snapshot', namelythe one most focused in space. This requires the introduction ofthe Entropy of source Image Contrast (EnIC)[Biryulina and Ryzhikov,1995]. The corresponding timeis associated with the event origin time, while the spatial coordinate of the source image maximum indicates the event location

Proper detectioninvolves estimations of a few parmeters such as 'sharpness'of a source image, self-consistency of P- and S-EWs identification,signal-to-noise ratios for both P- and S-EWs, and magnitude.In a post-event location/detection stage we may introduce finer griddingfor more refined epicenter localtion. Moreover, the EW-transforms alsoprovide us with estimates of peak P- and S- signal amplitudes within the'raw' trace filtered passband(s) and hence a mean for event magnitudeestimation (Mendi and Husebye, 1994). These parameters are also widelyused in seismic event classification studies. Source image scanning: Germany

Fig.2. Location of a weak (ML ~ 1.2) mining event from Harz area, Germany.The 'raw' records, prefiltered in the band 5-10 Hz, and the correspondingenvelopes [a)-c)] clearly indicate thebetter signal-to-noise ratio for EWs, than for Pg- and Sn/Lg- phases. The best source image 'snapshot',extracted automatically with time-scanning of EnIC is shown in {\bf d).} The three stations used from the German network CLZ (87 km), MOX (94 km) and CLL (116 km), are shown in e)with bulletin and our location marked bygreenandredrhombusesrespectively. Differential epicenter parameters are 0.01 N (latitude) and0.02 E (longitude).}The above type of automatically extracted seismicrecord parameters are well-suited for advanced network training. This canaddress problems such as more refined EW-velocity estimates. eventmagnitudes, event classification parameters and relative contributionsof individual stations in a network. Since our automatic event locationscheme 'works' with P- and S-wavelet maxima, the detectability of weakevents is excellent as demonstrated in Figure 2. Despite the low-frequencynature of the EW-wavelets the event location accuracy is also verygood as shown in Figure 3. Events at the network area periphery

Fig.3. Automaticlocation of 7 seismic events in the Titania mine on the south coastof Norway ( red box below). The stationsused, part of the Norwegian Seismograph Network, are marked by triangles.The upper right corner shows a zoom display of the mining area (grid unithere is approximately 5 km). Our solutions are shown by 'ringed' asteriskswhile the corresponding bulletin solutions are marked by asterisks only.The location of the mine itself by a box. The axizes of confidenceellipses are ~3 times shorter for the automatic scheme than for analystsolutions. No a priori crustal information is used in our analysis.Concluding remarksHere we have used the expression " location inreal time", since the time involved in processing is smallcompared to the travel time from source to receiver. In our caseit takes about 4 minutes for signal to reach the most remote station, whilethe location/detection algorithm takes only a few seconds of computertime to analyze 5-minute record segments from 10 stations.Our processing scheme has been tested on weak events (Germanyand Norway - e.g. with Figure 2 and 3) ,interfering events (Germany) ,but not on a continuous data stream from a network. The reasonfor this is that for the networks we have used only segments with known/presumedsignal presence are retained in permanent storage, therefore it was ratherdifficult to simulate continuous data stream. Nevertheless we are confidentthat our scheme will analyze continuous data stream in the same efficientmanner as for segmented data. In this contribution we have also describedand demonstrated a strategy for the training of regional seismicnetworks. The approach appears to be flexible and nearly invariant with respect to a crustal structure and thus should be easy transportable toany network even in adverse tectonic regions.The research reported here was supported by the US AirForce Office of Scientific Research, AFOSR Grant # F49620-94-1-0278. ReferencesBirylina, M.S., and G.A. Ryzhikov, 1995, Rytov-Borndecomposition in 3-D reflection seismics,in Extended abstracts EAEG and EAPG 57th Conference and Technical Exhibition, Glasgo, Vol.1, E-046.Kenneth, B.L.N., 1983. Seismic Wave Propagationin Stratified Media, Cambridge University Press, Cambridge, UK,342 pp.Mendi, C.D. and Husebye, E.S., 1994, Near real time estimationof magnitudes and moments for local seismic events, Annali diGeofisika, v. 37, pp. 365-382.Ryzhikov, G.A., and M.S. Birylina, 1995, 3Dnonlinear inversion by Entropy of Image Contrast optimization,NonlinearProcesses in Geophysics, vol. 2, no. 3/4, pp. 228-240.

Gzipped Postscript is here :~0.5 Mb, and PDF is here :~ 0.7 MbExtra figures/tables are here , and

A few extra comments on EW-transform can be found here and

.Also:Ryzhikov, Gennady A.; Biryulina,Marina S.; Husebye, Eystein S., 1995,Automatic event location using localnetwork data,in Proc. of the 17th Seismic ResearchSymposium on Monitoring a Comprehensive Test Ban Treaty, 12-15 09. 1995,Scottsdale, Arizona, pp. 389-400.Ryzhikov, Gennady A.; Biryulina,Marina S.; Husebye, Eystein S.,1995,Monitoring of a comprehensive test ban treaty - strategy for real time seismic event location. Proceedingsof the 10th Anniv. Finnish array workshop on GSETT-3 and IMS., 1 Lahti,Finland, 1995

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Technical	paper	describing	an
approach	to	automatic,	real-time	monitoring
of	regional	seismic	events,	based
on	the	theory	of	pulse
propagation	in	a	randomly	stratified
medium	with	waveguides.

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