Multiscale and layer-stripping wave-equation dispersion inversion of Rayleigh waves

Liu, Zhaolun; Huang, Lianjie

doi:10.1190/segam2018-2997500.1

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…To account for the complex physics and better explain the various wave phenomena, we develop a multiscale inversion strategy. It is worthwhile to mention that multiscale techniques have been widely used in convectional physicsbased FWI approaches [20,21,22] to handle different waves and preventing local minima. With this strategy incorporated, our new data-driven FWI is able to separately invert transmission and reflection waves in the seismic data.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study

Feng,

Lin,

Wohlberg

2021

Preprint

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Seismic full-waveform inversion (FWI), which applies iterative methods to estimate high-resolution subsurface detail from seismograms, is a powerful imaging technique in exploration geophysics. In recent years the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a non-convex problem, and can become stuck in a local minima due to the limited accuracy of the initial velocity maps, the absence of low frequencies in the measurements, the presence of noise, and the approximate modeling of the wave-physics complexity. To overcome these computational issues, we develop a multiscale datadriven FWI method based on the fully convolutional network (FCN). In preparing the training data, we first develop a realtime style transform method to create a large set of physically realistic subsurface velocity maps from natural images. We then develop two convolutional neural networks with encoder-decoder structure to reconstruct the low-and high-frequency components of the subsurface velocity maps, respectively. To validate the performance of our new data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. These numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time, and yield more accurate subsurface velocity map in comparison with conventional FWI.

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Section: Introductionmentioning

confidence: 99%

Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study

Feng,

Lin,

Wohlberg

2021

Preprint

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Wave equation dispersion inversion of surface waves recorded on irregular topography

Lin

Allam

et al. 2019

Geophysical Journal International

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Significant topographic variations will strongly influence the amplitudes and phases of propagating surface waves. Such effects should be taken into account, otherwise the S-velocity model inverted from the Rayleigh dispersion curves will contain significant inaccuracies. We now show that the recently developed wave-equation dispersion inversion (WD) method naturally takes into account the effects of topography to give accurate S-velocity tomograms. Application of topographic WD to demonstrates that WD can accurately invert dispersion curves from seismic data recorded over variable topography. We also apply this method to field data recorded on the crest of mountainous terrain and find with higher resolution than the standard WD tomogram.

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Wave-equation dispersion inversion of Love waves

Hanafy

Liu

et al. 2019

GEOPHYSICS

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We present a theory for wave-equation inversion of Love-wave dispersion curves, in which the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. Similar to inversion of Rayleigh-wave dispersion curves, the complicated Love-wave arrivals in traces are skeletonized as simpler data, namely, the picked dispersion curves in the [Formula: see text] domain. Numerical solutions to the SH-wave equation and an iterative optimization method are then used to invert these dispersion curves for the S-wave velocity model. This procedure, denoted as wave-equation dispersion inversion of Love waves (LWD), does not require the assumption of a layered model or smooth velocity variations, and it is less prone to the cycle-skipping problems of full-waveform inversion. We demonstrate with synthetic and field data examples that LWD can accurately reconstruct the S-wave velocity distribution in a laterally heterogeneous medium. Compared with Rayleigh waves, inversion of the Love-wave dispersion curves empirically exhibits better convergence properties because they are completely insensitive to the P-velocity variations. In addition, Love-wave dispersion curves for our examples are simpler than those for Rayleigh waves, and they are easier to pick in our field data with a low signal-to-noise ratio.

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Multiscale and layer-stripping wave-equation dispersion inversion of Rayleigh waves

Abstract: CitationLiu Z, Huang L (2018) Multiscale and layer-stripping waveequation dispersion inversion of Rayleigh waves. SEG Technical Program Expanded Abstracts 2018. Available: http:// dx.

Cited by 5 publications

References 21 publications

Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study

Multiscale Data-driven Seismic Full-waveform Inversion with Field Data Study

Wave equation dispersion inversion of surface waves recorded on irregular topography

Wave-equation dispersion inversion of Love waves

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