IRIS Newsletter,  vol. XV,  "No. 1. Spring 1996",  pp. 12-14  IRIS Newsletter           +  extra figures/comments

A novel approach to automatic monitoring
of regional seismic events.

Gennady A. Ryzhikov, Marina S. Biryulina,
and Eystein S.Husebye

University of Bergen, Norway

Summary

Improved event detection and location capability of regional networks can be achieved by developing and incorporating new concepts for seismic data analysis. Our strategy for automatic event location is tied to transforming  high-frequency  data to energetic wavelet envelopes (EW-transform) and is anchored in the theory of pulse propagation in a randomly stratified medium with waveguides.  Testing the new method on mining events from southern Norway, our epicenter determinations were far better than those derived by the analyst (bulletins). In Germany our scheme could handle very weak events for which interactive analysis failed. With the method it is possible to reduce the data volume for on-line transmission - by in situ (i.e. at the recording site)  resampling of records from digitizing frequency 40 - 80 Hz to 2 Hz. Our automatic location scheme is 'robust' in the sense that no crustal information is needed for its realization, once the network has been trained  through the development of proper EW travel time curves.

Event location/detection

The conventional approach to the problem of seismic event detection and 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 location analysis; a four-step process is rather clumsy and for poor to moderate signal-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 merge the above 4 tasks into one;  that is reformulate the problem as a joint event location/detection  problem.
The steps involved are: EW-association -> event pre-location -> EW-identification -> event detection. These steps in our real-time event localization algorithmare described 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-noise ratio is optimum for local/regional events, and then  subjected to the EW-transform  as illustrated in Figure 1 and 2.

P-, and S-Wavelet kinematics: Italy
 
Fig. 1. Example of energetic wavelet processing a) from stations (green circles) of the Italian network for an event 26.02.1995 {red rhombus}. b). Original waveform records and EW-envelopes. First arrivals are marked with flags c) The entire set of relevant dimensionless envelopes, ordered with respect to epicenter  distances. Amplitude isolines are drawn below. Note the linear spreading of EW ( c) ) which is typical of diffusion processes.d)P- and S-energetic wavelet travel times curves for Italian, Norwegian and German networks. The P- and S-EW maxima are automatically identified and picked at the event post-localization stage. The dispersion of maxima for the German network was essentially reduced after a brief period of 'network training'. The corresponding EW-velocities are 6.3 km/s and 3.5 km/s  for Italy/Norway and 5.9 km/s and 3.4 km/s for Germany.
The theoretical basis of EW-transform  is that pulse propagation in a randomly  stratified medium should create an energy wave train with diffusion in space and time, and therefore the energy distribution recorded by a station  can be interpreted as a random realization of a diffusion process in time domain. Two main wavefield intensity components occur in the vicinity of the free surface, namely primary energetic 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 independent of local crustal structure, focal depths and source mechanisms (Figure 1 Extra comments on kinematics of Energetic Wavelets, as expected from theory.

The validity of this EW-transform was tested on real data from Germany, Italy and Norway and the results are presented in Figure 1d. Similar phenomena would exist in a deterministic isotropic stratified medium with waveguides. [Kennett, 1983].  It is sufficient to transmit just EW-transformed traces  to the network HUB for subsequent event location and detection analysis.

Event location.

We pose the problem as a linear inversion  of EW- forms  with respect to an   artificial energetic source image:  i.e. an arbitrary space/time distribution of point-like  incoherent sources .  An infinite set of distributions exists that can fit  the observed data quite well, but there should be just one that approximates an impulse emitted at a proper time/space location. Note, that a network area is defined  by a minimum of 4-5 network stations - which are located at distances from the source of 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.An essential step in network training is estimation of P- and S- EW velocities, or  a part of self-learning of networks. This involves joint inversion of EW-forms from N events with respect to 3xN parameters  (epicenter coordinates, origin times) plus proper 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 which in reversed time 'emits' all  samples of the corresponding  EW-record into the network area  with appropriate P- and S-EW velocities. [Note, to avoid errors during estimation of  a 'true' amplitude, all EW-records are normalized  with respect to the corresponding maxima]. This procedure provides us with a source image in  the network monitoring area at each 0.5 - 1.0 sec (depends on a digitizing frequency of EW-records).
The normalized migration applied here is similar  to that described by Biryulina and Ryzhikov [Ryzhikov and Biryulina, 1995]

Source image dimensionless measure:
We extract the best source image 'snapshot',  namely the one  most focused in space. This requires the introduction of the Entropy of source Image Contrast (EnIC) [Biryulina and Ryzhikov,1995]. The corresponding time is  associated with the event origin time,  while the spatial  coordinate of the source image maximum  indicates the event location An example of the EnIC-scanning

Proper detection
involves 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 gridding for more refined epicenter localtion. Moreover, the EW-transforms also provide us with estimates of peak P- and S- signal amplitudes within the 'raw' trace  filtered passband(s) and hence a mean for event magnitude estimation (Mendi and Husebye, 1994). These parameters are also widely used 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 corresponding envelopes [a)-c)] clearly indicate the better 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  An example of the EnIC-scanning 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 by green and red rhombuses respectively. Differential epicenter parameters are 0.01 N (latitude) and 0.02 E (longitude).}
The above type of automatically extracted  seismic record parameters are well-suited for advanced network training. This can address problems such as more refined EW-velocity estimates.  event magnitudes, event classification parameters and relative  contributions of individual stations in a network. Since our automatic event location scheme 'works' with P- and S-wavelet maxima, the detectability of weak events is  excellent as demonstrated in Figure 2. Despite the low-frequency nature of the EW-wavelets  the event location accuracy is also very good as shown in Figure 3.
Events at the network area periphery
 
Fig.3.  Automatic location of 7 seismic events in  the Titania mine on the south coast of Norway ( red box below). The stations used, part of the Norwegian Seismograph Network, are marked by triangles. The upper right corner shows a zoom display of the mining area (grid unit here is approximately 5 km). Our solutions are shown by 'ringed' asterisks while the corresponding bulletin solutions are marked by asterisks only. The location of the mine itself by a box. The axizes of confidence ellipses are ~3 times shorter for the automatic scheme than for analyst solutions. No a priori crustal information is used in our analysis.
Concluding remarks

Here we have used the expression " location in real time",  since the time involved in processing is small compared  to the travel time from source to receiver. In our case it takes about 4 minutes for signal to reach the most remote station, while the location/detection  algorithm takes only a few seconds of computer time to analyze 5-minute record segments  from 10 stations.
Our processing scheme has been tested on weak events  (Germany and Norway - e.g. with Figure 2 and 3)  Comparisons between event locations, interfering  events (Germany)  An example of weak interfering events, but not on a continuous  data stream from a network.  The reason for this is that for the networks we have used only segments with known/presumed signal presence are retained in permanent storage, therefore it was rather difficult to simulate continuous data stream. Nevertheless we are confident that our scheme will analyze continuous data stream in the same efficient manner as for segmented data. In this contribution we have also described and demonstrated  a strategy for the training of regional seismic networks. The approach appears to be flexible and nearly invariant with  respect to a crustal structure and thus should be easy transportable to any network even in adverse tectonic regions.

The research reported here  was supported by the US Air Force Office of Scientific Research, AFOSR Grant # F49620-94-1-0278.
 

References

Birylina, M.S., and G.A. Ryzhikov, 1995, Rytov-Born decomposition 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 Propagation in Stratified Media, Cambridge University Press, Cambridge, UK, 342 pp.

Mendi, C.D. and Husebye, E.S., 1994, Near real time estimation of magnitudes  and moments for local seismic events, Annali di Geofisika, v. 37, pp. 365-382.

Ryzhikov, G.A., and M.S. Birylina, 1995, 3D nonlinear inversion by Entropy of Image Contrast  optimization, Nonlinear Processes in Geophysics, vol. 2, no. 3/4, pp. 228-240.
 
 

Gzipped Postscript is here ps.gz ~ 0.5 Mb :~0.5 Mb, and PDF is  here pdf ~ 0.7 Mb :~ 0.7 Mb
Extra figures/tables are here  Kinematics of Energetic Wavelets Time-scanning of EnIc and  Location capabilities of the approach
A few extra comments on EW-transform can be found here  Towards 'Wave Phenomena'-page  and EET: PDF ~490Kb.

Also:

Ryzhikov, Gennady A.; Biryulina, Marina S.; Husebye, Eystein S., 1995,
Automatic event location using local network data,
in Proc. of the 17th Seismic Research Symposium 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. Proceedings of the 10th Anniv. Finnish array workshop on GSETT-3 and IMS., 1 Lahti, Finland, 1995


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