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Coherence-weighted Wavepath Migration for Teleseismic Data Coherence-weighted Wavepath Migration for Teleseismic Data J. Sheng, G. T. Schuster, K. L. Pankow,

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Presentation on theme: "Coherence-weighted Wavepath Migration for Teleseismic Data Coherence-weighted Wavepath Migration for Teleseismic Data J. Sheng, G. T. Schuster, K. L. Pankow,"— Presentation transcript:

1 Coherence-weighted Wavepath Migration for Teleseismic Data Coherence-weighted Wavepath Migration for Teleseismic Data J. Sheng, G. T. Schuster, K. L. Pankow, J. C. Pechmann, and R. L. Nowack University of Utah Feb. 5, 2004

2 Motivation Given: Teleseismic data Goal: Local crustal structure Solution I: Receiver function (RF)

3 Principle of RF (Langston, 1977, 1979) P P PS Moho Vertical Comp. Radial Source history Green’s fun. Instrument

4 Problems Other phases generate artifacts Other phases generate artifacts Moho pPs pSs pPp

5 Motivation Given: teleseismic data Goal: local crustal structure Solution I: Receiver function (RF) Solution II: Xcorrelogram mig. (Xmig)

6 Principle of Xmig GhostP-wave DirectP-wave

7 Problems Incident angle usually > 30 deg. Incident angle usually > 30 deg. Irregular spacing Irregular spacing Low frequency and long source Low frequency and long source history history

8 Motivation Given: teleseismic data Goal: local crustal structure Solution I: Receiver function (RF) Solution II: Xcorrelogram mig. (Xmig) Solution III: Coherence-weighted WM

9 Coherence-weighted WM Outline Synthetic Test Earthquake Data Summary

10 Coherence-weighted WM Step 1: Calculate radial and vertical RF a. zero-phase traces b. source wavelet c. deconvolution

11 Coherence-weighted WM Step 2: Migrate RF and obtain ps, pPs, and pPp images Step 1: Calculate radial and vertical RF

12 Wavepath Migration Plane wave M ps (x)=RRF(T S -T P ) R X’ X X’ X X’ X P S M pPs (x)=RRF(T S +T P ) M pPp (x)=VRF(2T P )

13 Coherence-weighted WM Step 2: Migrate RF and obtain ps, pPs, and pPp images Step 1: Calculate radial and vertical RF Step 3: Coherence weight

14 Coherence-weighted WM M CW =W*Mps 0 60 0220 Depth (km) Distances (km) ps pPs pPp 0220 0220 0 60 Depth (km) 0220 Distances (km)

15 Coherence-weighted WM Outline Synthetic Test Earthquake Data Summary

16 060 0 220 Depth (km) Distances (km) Synthetic Model

17 Parameters (Synthetic) Plane P-wave incident at 40 deg. Plane P-wave incident at 40 deg. 221 Stations with 1km spacing 221 Stations with 1km spacing Source peak frequency 0.6 Hz Source peak frequency 0.6 Hz

18 0 70 Synthetic Seismogram Traveltime (sec.) VerticalRadial

19 0 20 Radial RF (Synthetic)

20 0 20 Traveltime (sec.) Vertical RF (Synthetic)

21 ps Image (Synthetic) 060 0 220 Depth (km) Distances (km)

22 pPs Image (Synthetic) 060 0 220 Depth (km) Distances (km)

23 pPp Image (Synthetic) 060 0 220 Depth (km) Distances (km)

24 CW Image (Synthetic) 060 0 220 Depth (km) Distances (km)

25 Coherence-weighted WM Outline Synthetic Test Earthquake Data Summary

26

27 Great Salt Lake 41.8 39.8 -113.5 -110.5 Latitude (deg.) Longitude (deg.) Station Map

28 Processing Parameters Processing Parameters120 50 270 200 Time (sec.) 50 sec. Passband: 0.2~0.6 Hz Water-level:0.001

29 Radial RF 020 0 200 Time (sec.) Distances (km)

30 020 0 200 Time (sec.) Distances (km) Vertical RF

31 060 0 200 Depth (km) Distances (km) ps Image

32 060 0 200 Depth (km) Distances (km) pPs Image

33 060 0 200 Depth (km) Distances (km) pPp Image

34 060 0 200 Depth (km) Distances (km) CW Image

35 Coherence-weighted WM Outline Synthetic Test Earthquake Data Summary

36 Summary ps, pPs, and pPp arrivals in RF can be migrated ps, pPs, and pPp arrivals in RF can be migrated to provide a different perspective. to provide a different perspective. CWWM can combine three images to correctly CWWM can combine three images to correctly image the reflector with attenuated artifacts. image the reflector with attenuated artifacts. This method can image the Moho at the depth This method can image the Moho at the depth consistent with previous studies. consistent with previous studies.

37 Acknowledgment I thank the sponsors of the 2003 UTAM Consortium for their financial support.


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