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Least-squares Migration and Least-squares Migration and Full Waveform Inversion with Multisource Frequency Selection Yunsong Huang Yunsong Huang Sept.

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Presentation on theme: "Least-squares Migration and Least-squares Migration and Full Waveform Inversion with Multisource Frequency Selection Yunsong Huang Yunsong Huang Sept."— Presentation transcript:

1 Least-squares Migration and Least-squares Migration and Full Waveform Inversion with Multisource Frequency Selection Yunsong Huang Yunsong Huang Sept. 5, 2013

2 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

3 Gulf of Mexico Seismic Survey m L m = d 1 1 2 2... N N Time (s) 6 X (km) 4 0 d Goal: Solve overdetermined System of equations for m Predicted dataObserved data

4 Details of Lm = d Time (s) 6 X (km) 4 0 d G(s|x)G(x|g) G(s|x)G(x|g)m(x)dx = d(g|s) Reflectivity or velocity model Predicted data = Born approximation Solve wave eqn. to get G’s m

5 Standard Migration vs Multisource Migration Benefit: Reduced computation and memory Liability: Crosstalk noise … Given: d 1 and d 2 Find: m Soln: m=L 1 d 1 + L 2 d 2 TT Given: d 1 + d 2 Find: m = L 1 d 1 + L 2 d 2 TT + L 1 d 2 + L 2 d 1 TT Soln: m = (L 1 + L 2 )(d 1 +d 2 ) T Romero, Ghiglia, Ober, & Morton, Geophysics, (2000) Src. imaging cond. xtalk

6 K=1 K=10 Multisource LSM & FWI Inverse problem: || d – L m || 2 ~~ 1 2 J = arg min m dd misfit m (k+1) = m (k) +  L  d ~T~T Iterative update: + L 1  d 2 + L 2  d 1 TT L 1  d 1 + L 2  d 2 TT

7 Brief Early History: Multisource Phase Encoded Imaging Romero, Ghiglia, Ober, & Morton, Geophysics, (2000) Krebs, Anderson, Hinkley, Neelamani, Lee, Baumstein, Lacasse, SEG Zhan+GTS, (2009) Virieux and Operto, EAGE, (2009) Dai, and GTS, SEG, (2009) Migration Waveform Inversion and Least Squares Migration Biondi, SEG, (2009)

8 Standard optimization for LSM/FWI Goal of the Study Multisource optimization for marine LSM/FWI Speed and quality comparison

9 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

10 Land Multisource FWI Fixed spread Simulation geometry must be consistent with the acquisition geometry

11 4 Hz8 Hz Marine Multisource FWI Simulated land data Observed marine data Mismatch solution with marine data wrong misfit Freq. encoding 8 Hz 4 Hz Blend Decode & mute purify 4 Hz8 Hz F.T., freq. selec. 4 Hz8 Hz

12 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

13 X Y Z kx ky  Phase-shift Migration Embarrassingly parallel domain decomposition ZZ  Multisource freq. sel. initially implemented here.

14 0 6.75 X (km) 0 Z (km) 1.48 a) Original b) Standard Migration Migration Images Migration Images (input SNR = 10dB) 0 6.75 X (km) c) Standard Migration with 1/8 subsampled shots 0 Z (km) 1.48 0 6.75 X (km) d) 304 shots/gather 26 iterations 304 shots in total an example shot and its aperture 3876152304 9.4 8.0 6.6 5.4 1 Shots per supergather gain Computational gain Conventional migration: SNR=30dB

15 3D Migration Volume 6.7 km True reflectivities 3.7 km Conventional migration 13.4 km 25616 256 shots/super-gather, 16 iterations 40 x gain in computational efficiency of OBS data 3.7 km

16 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

17 Transients Reduction nt 2nt causal periodic steady transient t t 8 Hz 4 Hz 2nt FDTD

18 periodic 0-lag correlate back-propagated residual wavefield steady transient forward-propagated source wavefield steady 2nt 1 t nt transient Computing FWI’s Gradient

19 Multisource FWI Freq. Sel. Workflow m (k+1) = m (k) +  L  d ~T~T For k=1:K end Filter and blend observed data: d  d d  Purify predicted data: d pred  d pred  d pred Data residual:  d=d pred -d Select unique frequency for each src

20 Quasi-Monte Carlo Mapping Standard Random permutation  index 1 60 Source index 1 60 Source index 1 60  index 1 60 Q.M. w/ repelling Coulomb force

21 Quasi-Monte Carlo Mapping 3 iterations 31 iterations

22 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

23 Frequency-selection FWI of 2D Marine Data Source freq: 8 Hz Shots: 60 Receivers/shot: 84 Cable length: 2.3 km Z (km) 0 1.5 0 6.8X (km) 4.5 1.5 (km/s)

24 FWI images Starting model Actual model Z (km) 0 1.5 Standard FWI (69 iterations) Z (km) 0 1.5 0 X (km) 6.8 Multisource FWI (262 iterations) 0 X (km) 6.8

25 Convergence Rates Waveform error Log normalized Log iteration number 1 0.025 1 262 69 by individual sources 1 supergather, Quasi-Monte Carlo encoding 3.8 x 1 supergather, standard encoding Same asymptotic convergence rate of the red and white curves Faster initial convergence rate of the white curve

26 Convergence Rates Velocity error Log normalized Log iteration number 1 0.35 1 262 69 1 supergather, standard encoding by individual sources 3.8 x Speedup 60 / 2 / 2 / 3.8 = 4 Gain 60: sources Overhead factors: 2 x FDTD steps 2 x domain size 3.8 x iterations 1 supergather, Quasi-Monte Carlo encoding

27 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

28 Source wavelet estimation 3D to 2D conversion of the data initial velocity model estimation Run FWI in multiscales Generate RTM, CIG & CSG images Workflow: FWI on GOM dataset

29 water surface delay:  t s r Received direct wave combined with ghost Source wavelet

30 Estimated w(t) Bandpass filtered to [0, 25] Hz Power spectrum of (b) 0.8 s

31 Source wavelet estimation 3D to 2D conversion of the data initial velocity model estimation Run FWI in multiscales Workflow: FWI on GOM dataset Generate RTM, CIG & CSG images

32 Source wavelet estimation 3D to 2D conversion of the data initial velocity model estimation Run FWI in multiscales Workflow: FWI on GOM dataset traveltime + semblance Generate RTM, CIG & CSG images

33 Source wavelet estimation 3D to 2D conversion of the data initial velocity model estimation Run FWI in multiscales Workflow: FWI on GOM dataset 0—6 Hz, 51 x 376 0—15 Hz, 101x 752 0—25 Hz, 201x 1504 Multisource Freq. Sel.: # steps: method: freq. band: grid size: 15 60 Gradient descent w/ line search. Stochastic gradient descent. Step size Mini-batch size: 2 496 shots  8 supergathers

34 Z (km) Traveltime FWI cost: 1 X (km) Z (km) FWIwMFS cost: 1/8 Velocity models obtained from:

35 FWIwMFS: V Q.M. – V random permutation Velocity difference due to encoding schemes: Q.M. vs standard X (km) Z (km) Model size: 18.8 x 2.5 km Source freq: 0--25 Hz Shots: 496Cable length: 6km Receivers/shot: 480 Baldplate GOM Dataset The freq. sel. scheme is resilient to specifics of encoding methods

36

37 Source wavelet estimation 3D to 2D conversion of the data initial velocity model estimation Run FWI in multiscales Workflow: FWI on GOM dataset Generate RTM, CIG & CSG images

38 X (km) Z (km) RTM image using traveltime tomogram

39 Z (km) X (km) RTM image using FWI tomogram

40 Z (km) X (km) RTM image using FWIwMFS tomogram

41 Zoomed views of the RTM images

42

43

44 CIGs for traveltime tomogram

45 CIGs for FWI tomogram

46 CIGs for FWIwMFS tomogram

47 Observed CSG 7 Time (s)

48 FWI predicted CSG 7 Time (s)

49 FWIwMFS predicted CSG 7 Time (s)

50 TRT predicted CSG 7 Time (s)

51 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

52 g s p L W First Fresnel Zone: | | + | | = | p s | + | p g | = L + /2 Wavepath Resolution (width)

53 Wavepath Resolution

54

55 IntroductionIntroduction Multisource Frequency SelectionMultisource Frequency Selection –Least-squares migration (LSM)  test on 2D and 3D synthetic data –Full Waveform Inversion (FWI)  test on 2D synthetic and field GOM data Resolutions for Wave Equation ImagingResolutions for Wave Equation Imaging SummarySummary Outline

56 Summary

57 Acknowledgements I thank –my advisor, Dr. Gerard T. Schuster, for his guidance, support and encouragement; –my committee members for the supervision over my dissertation; –the sponsors of CSIM consortium for their financial support; –my fellow graduate students for the collaborations and help over last 4 years.


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