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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, 2 Philip Maechling , 3 Yifeng Cui, 2 Scott Callaghan 1 University of Wyoming , 2 University of Southern California,

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  1. Rapid Centroid Moment Tensor (CMT) Inversion in 3D Earth Structure Model for Earthquakes in Southern California 1En-Jui Lee, 1Po Chen,2Thomas H. Jordan, 2Philip Maechling, 3Yifeng Cui, 2Scott Callaghan 1University of Wyoming, 2University of Southern California, 3San 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 Broadband Data Waveforms Automatic window picking Selected Windows NCC between data & synthetic Measurements (NCC, dt, lnA) Bayesian inference Optimal CMT Solution

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

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

  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 Broadband Data Waveforms Automatic window picking Selected Windows NCC between data & synthetic Measurements (NCC, dt, lnA) Bayesian inference Optimal CMT Solution

  11. Bayesian inference • 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 (ML=3.13) • Comparison of relocated depths • Comparison of magnitude estimations

  14. Synthetic waveform comparisons

  15. An example of ML=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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