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3D Seismic Imaging based on Spectral-element Simulations and Adjoint Methods

1 st QUEST Workshop, Sep 2010. 3D Seismic Imaging based on Spectral-element Simulations and Adjoint Methods. Qinya Liu Department of Physics University of Toronto. Collaborations with Carl Tape, Alessia Maggi, Jeroen Tromp, Dimitri Komatitsch and many others.

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3D Seismic Imaging based on Spectral-element Simulations and Adjoint Methods

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  1. 1st QUEST Workshop, Sep 2010 3D Seismic Imaging based on Spectral-element Simulations and Adjoint Methods Qinya Liu Department of Physics University of Toronto Collaborations with Carl Tape, Alessia Maggi, Jeroen Tromp, Dimitri Komatitsch and many others

  2. Numerical Simulation of Seismic Wave Propagation based on SEM • SPECFEM3D (GLOBE, SESAME) packages are available through CIG website: http://www.geodynamics.org/cig/software/ • Practical Sessions on Friday 4-6 pm • Princeton University's Near Real Time Simulation of Global Seismic Events Portal (Mw > 5.5) http://shakemovie.princeton.edu/

  3. Sep 9, 2010 Mw=6.2 Offshore Chile Event S362ANI model (Kustowski 2008)

  4. Inverse ProblemI. Define Misfit Function Travel time Misfit Other types of Measurements: waveform misfit (Tarantola 84,05) cross-correlation travel time (Luo & Schuster 91) frequency-dependent phase and amplitude (e.g. Zhou et al 04, Fichtner 09 et al, Chen et al 04) How to identify phases?

  5. Window Selection: FLEXWIN Available through CIG Maggi et al (2008)

  6. Inverse ProblemII. Derivative of Misfit Tromp et al 05 Tape et al 08 Event kernel

  7. One measurement Construction of Kernels (2D) Based on two SEM simulations - same for multiple Source-receiver Pairs - afternoon practical session Tape et al (2008)

  8. Inverse Problem II. 2nd order derivative – Hessian matrix? We need kernels for individual measurements! Numerically expensive when 3D simulations are used. Similarly, for multiple events: Nonlinear conjugate gradient method LS

  9. Advantages and Disadvantages • 3D initial model • Accurate 3D Green's functions • Accurate sensitivity kernels • More phases • Computationally intensive: 3xE simulations/iteration • More iterations needed: 6 CG iterations ~ 1 iteration with Hessian

  10. Southern California Crust Initial model: CVM-H (Tape et al. 09, 10)

  11. Tape et al 09,10

  12. Waveform Fits

  13. Reflections • Model error estimation (sample the posterior model distribution) • Faster convergence? (source subspace methods) • Parameterization • Restrictions: • Sources and receivers in the same domain (local events) • Tele-seismic data for local structure? • Array data?

  14. Solutions I:New dataset: micro-seismic noise correlation Weaver, 2005

  15. Ambient Noise for SoCal Black: cc data (10-20 s) Red: 3D Green's function Blue: synthetic 3D cc based on Tromp et al 10

  16. Tele-seismic Data • High-resolution regional scattered-wave imaging using coda waves of main seismic phases Scattered-wave imaging, GRT Receiver Functions e.g. Zhu & Kanamori (2000) e.g. Bostock et al (2001)

  17. Sensitivity kernels for tele-seismic phases Global SEM simulations run regularly at accuracy up to 20 seconds, but become extremely demanding at shorter periods. Representation Theorem (Aki & Richards, 2002)

  18. Representation Theorem

  19. Toy Problem Re-generate Forward field by Kirchhoff Integral

  20. S Kernel Interaction between Forward wave field and Adjoint wave field

  21. Kernel for S-coda Waves

  22. HP Computing Facilities Data Theory

  23. The End

  24. Numerical simulation of wave propagation in 3D media both at local and regional scales. Komatitsch & Tromp (02a,b) Komatitsch et al (04) Forward simulation Adjoint Simulation Kernel Calculation (Liu & Tromp 06,08)

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