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Multisource Full Waveform Inversion of Marine Streamer Data with Frequency Selection Yunsong Huang and Gerard Schuster KAUST
Multisource Full Waveform Inversion of Marine Streamer
Data with Frequency Selection
Yunsong Huang and Gerard SchusterKAUST
• Aim of the study• Multisource
– Mismatch solution with marine data• Low-discrepancy frequency coding• Numerical results • Conclusions
Outline
WorkflowPreprocessing
Standard optimization
for FWI
Aim of the Study
Multisource optimization
for FWI
Speed and quality
comparison
• Aim of the study• Multisource
– Mismatch solution with marine data• Low-discrepancy frequency coding• Numerical results • Conclusions
Outline
MultisourceFixed spread
Simulation geometry consistent with the acquisition geometry
Multisource
Simulated land data
Observedmarine data
Mismatch solution with marine data
wrong misfit
Freq. encoding Decode & mute
8 Hz4 Hz
4 Hz 8 Hz
Blend
F.T.,freq. selec.
• Aim of the study• Multisource
– Mismatch solution with marine data• Low-discrepancy frequency coding• Numerical results • Conclusions
Outline
encodingStandard
Freq
uenc
y in
dex
160
Freq. #60assigned tosource #31
crow
ded
vacant
Low-discrepancy Frequency Encoding
Source index1 60
Prefers uniformity in freq. assignment /
encoding.
Low-discrepancy Frequency Encoding
Freq
uenc
y in
dex
160
Source index1 60 Source index1 60
Low-discrepancyencodingencoding
Standard
Freq
uenc
y in
dex
160
• Aim of the study• Multisource
– Mismatch solution with marine data• Low-discrepancy frequency coding• Numerical results • Conclusions
Outline
Frequency-selection FWI of 2D Marine Data
• Source freq: 8 Hz• Shots: 60• Receivers/shot: 84 • Cable length: 2.3 km
Z (k
m)
01.
5
0 6.8X (km)
4.5
1.5
(km/s)
Convergence RatesWaveform error
Log
nor
mal
ized
Log iteration number
10.
025
1 26269
by individual sources1 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
Convergence RatesVelocity error
Log
nor
mal
ized
Log iteration number
10.
35
1 26269
1 supergather,
standard encoding
by individual sources 3.8 x
Speedup60 / 2 / 2 / 3.8 = 4
Gain• 60: sourcesOverhead factors:• 2 x FDTD steps• 2 x domain size• 3.8 x iteration
number1 supergather, low-discrepancy encoding
Convergence RatesVelocity error (normalized)
10.
75
iteration number1 10
by individual sources
1 supergather,
standard encoding
H
LSlew rate = H/L
1 supergather, low-discrepancy encoding
Convergence RatesVelocity error (normalized)
10.
75
iteration number1 10
standard encoding
Slew rate = H/L
Low-discrepancy encoding is
12% to 3x faster initially than
Standard encoding
H
L
FWI imagesStarting modelActual model
Z (k
m)
01.
5
Standard FWI(69 iterations)
Z (k
m)
01.
5
0 X (km) 6.8
Multisource FWI(262 iterations)
0 X (km) 6.8
• Frequency selection is implemented in FDTD– 2 x time steps per forward or backward
modeling• Low-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 model
Conclusions
Thank you!
• At lower (say 1/2) frequencies, the frequency selection strategy sees fewer frequency resources, but 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
