Usage of joint rating functions for seismic phase association and event location

1. Usage of joint rating functions for seismic phase association and event location Asming V.E., Prokudina A.V., Nakhshina L.P. Kola Regional Seismological Centre Russia

2. MAIN IDEA • When a seismic phase is detected a set of azimuth-dependent rating functions for the phase can be computed : • a) Polarization functions • PP(α) – estimation of hypothesis that the phase is P coming from α; • PS(α) – estimation of hypothesis that the phase is S coming from α; • b) Beamforming (sum of shifted array channels) • B(α,V) – estimation of hypothesis that the phase is coming from α with velocity V • The functions are usually used to determine phase types. • We propose to use combinations of the function for several phases simultaneously to check hypothesis that the phases are of the same seismic event • The approach can be useful for single stations (3C and arrays) as well as for sparse networks

3. Joint analysis of couples of phases detected at the same station The idea: Detect some phases (say, using STA/LTA) For each couple of phases check a hypothesis that the 1st one is P and the 2nd one is S from the same event. If the estimation is greater than a threshold then locate the event by the backazimuth and S-P time difference Implementation: Joint polarization analysis Joint beamforming Usage some penalties and Bayesian belief networks to take into account recording (envelope) shapes

4. Joint polarization analysis S P Red line : R(α) is normalized horizontal motion Blue line : Cz(α) is correlation between horizontal and vertical motion Joint estimation for backazimuth α Estimations of phase kinds : PP()=(1 + R())(1+CZ())/4 PS()=(1 + R(+90))(1-CZ(+90))/2 Estimation of the hypothesis that phase A is P and B is S from the same event: where Penalty is some functional dependent on the phases

5. Joint beamforming for an array Checking the hypothesis that the 1st phase is P and the 2nd one is S from the same event by an array sensors. S P ΔTs = ΔTp·(Vp/Vs) upper layer ΔTp The idea is to make beamforming simultaneously for P and S fragments and use related time delays for P and S.

6. Joint beamforming for an array The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is: Where (tA1,tA2) is time interval for P candidate, (tB1,tB2) is for S candidate; Zi(t) : samples of i-th sensor Δti(α,V,Vupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor. It depends also on upper layer velocity Vupper if we take into account sensors elevations R=Vp upper/Vs upper : P and S velocities ratio under the array If the array includes a 3-component sensor we can multiply JB(α,Vp) by the polarization estimation PPASB(α). The final estimation is Rating(α,Vp)=PPASB(α)·JB(α,Vp)

7. For the algorithm it is important to know Vp and Vp/Vsunder an array We have selected several events, picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (Vp/Vs) upper: Events from North-West. Maximum is not realistic! Vp/Vs~1 ! SPI array, Spitsbergen Storfjorden events. Maximum at Vp=5.5 km/sec and Vp/Vs=1.8 SPI array, Spitsbergen

8. Joint analysis of couples can screen out non-realistic candidates to be phases Dist to the centre P ampl Phase before P S ampl Centre of envelope P candidate S candidate • Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples. • In this case the following penalties are used to decrease the rating: • Due to presence of a phase before the P candidate; • Due to large time difference between the envelope centre and the S candidate; • Due to huge amplitude ratio of the P and S candidates; • Such penalties and/or rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks.

9. An example of Bayesian belief network analyzing couples of phases Linear polarization of 1st phase Yes/No V apparent 1st phase>VpMin Yes/No Is compatible phase before the 1st ? Yes/No Is the 1st phase P-wave ? Yes/No 1st and 2nd phases are compatible by polarization Yes/No 1st and 2nd phases are compatible by joint beamforming Yes/No Is this P-wave of event beginning ? Yes/No Phases are connected in envelope Yes/No The couple is P/S of the same event Yes/No Final decision

10. Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring (UDL program) Storfjorden is a seismically active zone at distances 100-150 km from SPI array • STA-LTA detector for generalized envelopes; • P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area); GBF UDL Manual analysis has shown that at least 95% of the events are true. Linear slope of the frequency-magnitude curve for M>-0.2 leads us to conclusion that we detect the most part of the events with M>-0.2. SPI data had been processed since 2008

11. Near real-time Storfjorden monitoring http://www.krsc.ru/storfjorden (since 12.01.2011)

12. Joint beamforming by a couple of arrays (idea) Location of Zapolyarny explosion by two arrays : AP0 (Apatity) and ARC (Norway). When P onsets are picked the line can be drawn on which the event occurred (P-P). Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same.

13. Example of joint beamforming by couple of phases : S(AP0) and S(ARC) for Zapolyarny explosion X axis : length (L) along S-S line Y axis : apparent velocity (km/sec) (the same for both arrays) Red points : uncertainty area (B>0.99 Bmax) Maximizing function : Where l is length along S-S line, V is apparent velocity, α is backazimuth to an array dependent on l. B is amplitude of an array beam (sum of time-shifted channels) In more complicated cases V apparent can be calculated using travel-time model.

14. Perspectives of joint data processing • The European Arctic is area with a very sparse seismic network. But huge amount of seismic events with complicated shapes of wave forms occur here. Every known algorithm of detection and location makes a lot of false alarms. It is difficult to obtain a realistic picture of local seismicity. • We suppose to make a new data processing system based on the following principles: • A collection of algorithms where each one estimates probability of some fact (for example, that a part of recording contains an onset, that an onset is P wave, that a part of envelope corresponds to a seismic event etc.). The algorithms could be very different (ordinary STA/LTA, envelope-based, neural networks etc.) but operate with standard data types; • A system that calls the algorithms according to absence or presence of some data types (3C data, array data, envelopes etc.) • A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not, are onsets found, what are the waves etc.)

15. Thank You for attention !