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Wei Dai, Ge Zhan, Xin Wang, and G. Schuster KAUST and University of Utah Multi-source Least Squares Migration and Waveform Inversion.

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Presentation on theme: "Wei Dai, Ge Zhan, Xin Wang, and G. Schuster KAUST and University of Utah Multi-source Least Squares Migration and Waveform Inversion."— Presentation transcript:

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2 Wei Dai, Ge Zhan, Xin Wang, and G. Schuster KAUST and University of Utah Multi-source Least Squares Migration and Waveform Inversion

3 Outline Fast Multisource+Precond. TheoryFast Multisource+Precond. Theory Multisource Waveform InversionMultisource Waveform Inversion Multisource Least Squares MigrationMultisource Least Squares Migration ConclusionConclusion

4 RTM Problem & Possible Soln. Problem: RTM computationally costlyProblem: RTM computationally costly Partial Solution: Multisource LSM RTMPartial Solution: Multisource LSM RTM 3 Preconditioning speeds convergence by factor 2-3 LSM reduces crosstalk

5 Multisource Migration: m mig =L T d Forward Model: m =[L T L] -1 L T d Multisrc-Least Sq. Migration : Multisource Least Squares Migration m’ = m - L T [Lm - d] f ~ [L T L] -1 f Steepest Descent Preconditioned d +d =[ L +L ]m 1221 multisource preconditioner multisource modeler+adjoint L {d {

6 Outline Fast Multisource+Precond. TheoryFast Multisource+Precond. Theory Multisource Waveform InversionMultisource Waveform Inversion Multisource Least Squares MigrationMultisource Least Squares Migration ConclusionConclusion

7 9 SEG/EAGE Salt Model 016 4 0 Depth (km) X (km) 1500 4500 Velocity (m/s) Time (s) X (km) CSG Multisource CSG

8 Random Time Shifted CSG and Add : m’ = m - L T [Lm - d] + reg. Multisource Least Squares Migration Workflow f d =d + d 1 2 Compute Preconditioner : f = [L T L] Iterate Preconditioned Regularized CG: *f = Generate ~200 CSGs, Born approx: d and d 1 2

9 3 Z (km) 8 0 1.5 Model LS M (30 its) Kirchhoff Migration Model, KM, and LSM Images 0 3km LSM 10 srcs (5 its) KM 10 Srcs LSM 10 srcs (30 its) 90x1x1.5x 9x 0.1x

10 3 Z (km) 9 0 1.5 Model Kirchhoff Migration LSM 10 srcs (5 its) KM 40 Srcs LSM 40 srcs (30 its) 90x1x1.5x 2.5x 0.02x Model, KM, and LSM Images LS M (30 its) 0 3km

11 Did Deblurring Help? CG deblurring Standard precond. CG 0 1.4 030 Iteration # ||Data Residual||

12 Conclusions 1. Empirical Results: Multisrc. LSM effective in suppressing crosstalk for up to 40 source supergather, but at loss of subtle detail. Did not achieve breakeven 2.5x > 1x. achieve breakeven 2.5x > 1x. 3. Blending Limitation: Overdetermined>Undetermined 2. Deblurring precond. >> Standard 1/r precond. 2 T 4. Future: Better deblurring [L L] and regularizer

13 Outline Fast Multisource+Precond. TheoryFast Multisource+Precond. Theory Multisource Waveform InversionMultisource Waveform Inversion Multisource Least Squares MigrationMultisource Least Squares Migration ConclusionConclusion

14 Multiscale Waveform Tomography 1. Collect data d(x,t) 2. Generate synthetic data d(x,t) by FD method syn. 3. Adjust v(x,z) until ||d(x,t)-d(x,t) || minimized by CG. syn. 2 4. To prevent getting stuck in local minima: a). Invert early arrivals initially a). Invert early arrivals initially mute 7 b). Use multiscale: low freq. high freq. b). Use multiscale: low freq. high freq.

15 Multi-Source Waveform Inversion Strategy (Ge Zhan) Generate multisource field data with known time shift Generate synthetic multisource data with known time shift from estimated velocity model Multisource deblurring filter Using multiscale, multisource CG to update the velocity model with regularization Initial velocity model 144 shot gathers

16 12-Source Waveform Tomogram 0X(m)1910 0 Z (m) 595 5000 2000 m/s Single-Source Waveform Tomogram 0X(m)1910 0 Z (m) 595 5000 2000 m/s Marmousi Model Acoustic Marmousi Model and Multiscale Waveform Inversion 12x Smooth Starting Model 50 iterations

17 2000 12-Source Misfit Gradient vs Deblurred Gradient Standard 12-Src Gradient 19.5% Error 7.1% Error Deblurred 12-Src Gradient

18 Residual Gradient vs # of Shots

19 Summary Multisource+Precond. +CG Reduces CrosstalkMultisource+Precond. +CG Reduces Crosstalk Multisource Waveform Inversion: reducesMultisource Waveform Inversion: reduces computation by 12x for Marmousi computation by 12x for Marmousi Multisource LSM: Reduces LSM computation $$Multisource LSM: Reduces LSM computation $$ but still costs > standard mig. but still costs > standard mig. Problem: Need Formulas for S/N vs dxProblem: Need Formulas for S/N vs dx Potential O(10) speedup with 3DPotential O(10) speedup with 3D

20 Outline Fast Multisource+Precond. TheoryFast Multisource+Precond. Theory Multisource Waveform InversionMultisource Waveform Inversion Multisource Least Squares MigrationMultisource Least Squares Migration Multisource MVAMultisource MVA ConclusionConclusion


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