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Multisource Full Waveform Inversion of Marine Streamer Data with Frequency Selection Multisource Full Waveform Inversion of Marine Streamer Data with Frequency Selection Yunsong Huang and Gerard Schuster Yunsong Huang and Gerard SchusterKAUST

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Aim of the studyAim of the study MultisourceMultisource –Mismatch solution with marine data Low-discrepancy frequency codingLow-discrepancy frequency coding Numerical resultsNumerical results ConclusionsConclusions Outline

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Workflow Preprocessing Standard optimization for FWI Aim of the Study Multisource optimization for FWI Speed and quality comparison

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Aim of the studyAim of the study MultisourceMultisource –Mismatch solution with marine data Low-discrepancy frequency codingLow-discrepancy frequency coding Numerical resultsNumerical results ConclusionsConclusions Outline

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Multisource Fixed spread Simulation geometry consistent with the acquisition geometry

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

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Aim of the studyAim of the study MultisourceMultisource –Mismatch solution with marine data Low-discrepancy frequency codingLow-discrepancy frequency coding Numerical resultsNumerical results ConclusionsConclusions Outline

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encoding Standard Frequency index 1 60 Freq. #60 assigned to source #31 crowded vacant Low-discrepancy Frequency Encoding Source index 1 60 Prefers uniformity in freq. assignment / encoding.

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Low-discrepancy Frequency Encoding Frequency index 1 60 Source index 1 60 Source index 1 60 Low-discrepancy encoding Standard Frequency index 1 60

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Aim of the studyAim of the study MultisourceMultisource –Mismatch solution with marine data Low-discrepancy frequency codingLow-discrepancy frequency coding Numerical resultsNumerical results ConclusionsConclusions Outline

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

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Convergence Rates Waveform error Log normalized Log iteration number by individual sources 1 supergather, low-discrepancy 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

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Convergence Rates Velocity error Log normalized Log iteration number 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 iteration number 1 supergather, low-discrepancy encoding

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Convergence Rates Velocity error (normalized) iteration number 1 10 by individual sources 1 supergather, standard encoding H L Slew rate = H/L 1 supergather, low-discrepancy encoding

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Convergence Rates Velocity error (normalized) iteration number 1 10 standard encoding Slew rate = H/L Low-discrepancy encoding is 12% to 3 x faster initially than Standard encoding H L

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

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Frequency selection is implemented in FDTDFrequency selection is implemented in FDTD –2 x time steps per forward or backward modeling Low-discrepancy frequency encodingLow-discrepancy frequency encoding –affects no asymptotic rate of convergence –helps to reduce model error in the beginning of simulation 4x speedup for the multisource FWI on the synthetic marine model4x speedup for the multisource FWI on the synthetic marine model Conclusions

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Thank you!

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At lower (say 1/2) frequencies, the frequency selection strategy sees fewer frequency resources, butAt lower (say 1/2) frequencies, the frequency selection strategy sees fewer frequency resources, but Computation cost: Computation cost: –(Nx x Nz) x Ns x Nt is reduced by 1/16, –since each factor is halved. This part does not degrade the overall speedup much. In the case of multiscale

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