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Rapid Centroid Moment Tensor (CMT) Inversion in 3D Earth Structure Model for Earthquakes in Southern California 1 En-Jui Lee, 1 Po Chen, 2 Thomas H. Jordan,

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Presentation on theme: "Rapid Centroid Moment Tensor (CMT) Inversion in 3D Earth Structure Model for Earthquakes in Southern California 1 En-Jui Lee, 1 Po Chen, 2 Thomas H. Jordan,"— Presentation transcript:

1 Rapid Centroid Moment Tensor (CMT) Inversion in 3D Earth Structure Model for Earthquakes in Southern California 1 En-Jui Lee, 1 Po Chen, 2 Thomas H. Jordan, 2 Philip Maechling, 3 Yifeng Cui, 2 Scott Callaghan 1 University of Wyoming, 2 University of Southern California, 3 San Diego Supercomputer Center

2 Overview Introduction Methodology Results Reduce source errors for tomo. (Near) Real-time application Summary

3 Introduction Earthquake-prone area 244 broadband stations Seismic hazard analysis Realistic interpretation of geological structures

4 Introduction 3D updated velocity model: CVM4SI2 Improved model  better source estimations Our current tomography results 4:45 pm Ballroom D

5 Source inversion Automatic window picking Broadband Data WaveformsSelected Windows NCC between data & synthetic Measurements (NCC, dt, lnA) Optimal CMT Solution Bayesian inference

6 Automatic window picking Less heterogeneity effects : P, Pnl, S & surface waves Continuous wavelet transform (CWT) Topological watershed (TW)

7 Source inversion Automatic window picking Broadband Data WaveformsSelected Windows NCC between data & synthetic Measurements (NCC, dt, lnA) Optimal CMT Solution Bayesian inference

8 Synthetic seismograms Any M is linear combination of elementary seismograms M1 ~ M6 Different subgroups can represent the specific solutions 1 Kikuchi & Kanamori, 1991

9 Measurements NCC between data windows & synthetic seismogram  NCC, dt, lnA

10 Source inversion Automatic window picking Broadband Data WaveformsSelected Windows NCC between data & synthetic Measurements (NCC, dt, lnA) Optimal CMT Solution Bayesian inference

11 Apply the Bayesian inference to different type of measurements (Ncc, dt and lnA) Assuming the measurements are independent Select the CMT with highest probability

12 Example of Yorba Linda event 2002/09/03 Mw 4.3

13 Results Compare synthetic waveforms between 1D multi-layer and 3D models An example of small earthquake (M L =3.13) Comparison of relocated depths Comparison of magnitude estimations

14 Synthetic waveform comparisons

15 An example of M L =3.13 earthquake

16 Comparison of relocated depths

17 An example of depth comparison

18 Comparison of magnitude estimations

19 Reduce source errors for tomography

20 (Near) Real-time application Using 10 sec of 2008 Chino Hills earthquake find an optimal solution in 20 secs (4 cores)

21 Summary Rapid and accurate CMT solution –Store RGTs  rapid –3D velocity structure  accurate waveforms By applying Bayesian inference  provide uncertainty estimates for the source parameters Potential application for (near) real-time source inversion  (near) real-time ground motion forecast Probabilistic Seismic Hazard Analysis (PSHA) - CyberShake

22 Thank You! Go to http://www-rcf.usc.edu/~pochen/ for PDF preprints and reprints


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