3D hybrid-domain full waveform inversion on GPU

Liu, Lu; Ding, Renwei; Liu, Hongwei; Liu, Hong

doi:10.1016/j.cageo.2015.06.017

Cited by 38 publications

(4 citation statements)

References 28 publications

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“…In order to harness the computational efficiency of the time‐domain full‐waveform inversion (FWI) and multi‐scale inversion of frequency‐domain FWI, the hybrid‐domain FWI is utilized. This method employs time‐domain forward modelling coupled with frequency‐domain inversion (Liu, Ding, et al., 2015; Sirgue et al., 2008; Zhang et al., 2018). In this paper, the inversion is implemented in this way.…”

Section: Methodsmentioning

confidence: 99%

An efficient elastic full‐waveform inversion of multiple parameters with ocean‐bottom seismometer data

Huang,

Liu

2024

Geophysical Prospecting

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Detailed knowledge of the subsurface elastic properties might provide much‐needed insight into the subduction‐zone structure and the estimation of reservoir parameters. Compressional and shear wave velocities can be inverted by elastic full‐waveform inversion using multicomponent ocean‐bottom seismometer data. However, this process is computationally intensive, requiring massive, repeated simulations scaling with the number of sources. Although source–receiver reciprocity can streamline elastic full‐waveform inversion, it cannot be applied by directly interchanging the positions of sources and ocean‐bottom seismometers. To reduce the computational cost, we develop a source–receiver reciprocal elastic full‐waveform inversion, in which the reciprocity in multicomponent data can be accomplished by transforming the data matching from the original geometry to a specific form under the reciprocal geometry. This approach significantly reduces computational costs from twice the number of sources, as seen in conventional elastic full‐waveform inversion, to a scale with the count of ocean‐bottom seismometers. The tests on synthetic data verify that the proposed reciprocal elastic full‐waveform inversion maintains accuracy while improving computational efficiency, and further application on the field data from East China Sea also showcases the efficiency of the proposed method, resulting in a speed‐up of 10 times.

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Section: Methodsmentioning

confidence: 99%

An efficient elastic full‐waveform inversion of multiple parameters with ocean‐bottom seismometer data

Huang,

Liu

2024

Geophysical Prospecting

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“…Recent efforts to harness the computational capabilities of graphics processing units for FWI (e.g. Mao, Wu and Wang, 2012;Liu et al, 2015;Yang, Gao and Wang, 2015) will only serve to accelerate the adoption of FWI as a viable approach to evaluate near-surface geologic materials for engineering purposes.…”

Section: Computational Run Time Misfits and Practical Considerationsmentioning

confidence: 99%

Numerical comparison of Rayleigh and Love full waveform inversion in characterizing soil spatial variability for near‐surface engineering applications

Mahvelati

Coe

2020

Near Surface Geophysics

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The use of surface wave testing for near‐surface engineering applications has increased in recent years. Typical surface wave analysis is based on the dispersion of surface waves in one‐dimensional layered models. One‐dimensional models are inappropriate for measurements at sites with appreciable lateral variability, a likely scenario in many engineering applications. Use of such models can subsequently undermine the reliability and accuracy of the surface wave results. Full waveform inversion (FWI) is a high‐resolution imaging technique that is proven to outperform the conventional dispersion‐based analysis of surface waves. Much of the near‐surface literature has focused on full waveform inversion of Rayleigh waves developed by the interaction of primary‐ and vertically polarized shear waves (P‐SV), and the capabilities of surface waves generated by horizontally polarized shear waves (Love waves) in mapping near‐surface spatial variabilities have not been fully explored. In this numerical study, full waveform inversion of Rayleigh and Love waves was performed on two different spatially correlated Gaussian random fields (mean VS of 200 and 500 m/s) to mimic the natural spatial variability of geologic materials. Each soil structure was produced at a low and high level of stiffness variability. Two sources with different frequency contents, 25 and 50 Hz, were used to evaluate the effects of source characteristics on the resolution of Rayleigh and Love waveform inversions. The inverted results from the high‐velocity domain demonstrated that Love waveform inversion using high‐frequency seismic sources outperforms Rayleigh full waveform inversion in detecting the shape and the velocities of horizontally deposited geologic materials. Results from the low‐velocity domain also confirmed that Love full waveform inversion was comparable or superior to Rayleigh full waveform inversion, though the performance difference was less significant. However, the 25‐Hz frequency inversions yielded superior results than the 50‐Hz frequency inversions for the low‐velocity domain because the dominant wavelength of the high‐frequency signals becomes so small that it offers an impractically small investigation depth.

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“…The application of NVIDIA GPUs in seismic image processing has significantly improved computing efficiency, for instance, in time migration and RTM and waveform inversion. [15][16][17], especially RTM, because it has the highest calculation accuracy in the seismic migration but is also the most time-consuming. Therefore, when GPUs appeared, it was used to accelerate RTM computation [18] by includingthe useof random boundary instead of storing wavefields to save GPU global memory [19], as well as some GPU computing strategies for the calculation of specific form of RTM tilted transversely isotropic TTI RTM and Q-RTM [20,21].…”

Section: Introductionmentioning

confidence: 99%

Calculation of Surface Offset Gathers Based on Reverse Time Migration and Its Parallel Computation with Multi-GPUs

Liu

2021

Applied Sciences

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As an important method for seismic data processing, reverse time migration (RTM) has high precision but involves high-intensity calculations. The calculation an RTM surface offset (shot–receiver distance) domain gathers provides intermediary data for an iterative calculation of migration and its velocity building. How to generate such data efficiently is of great significance to the industrial application of RTM. We propose a method for the calculation of surface offset gathers (SOGs) based on attribute migration, wherein, using migration calculations performed twice, the attribute profile of the surface offsets can be obtained, thus the image results can be sorted into offset gathers. Aiming at the problem of high-intensity computations required for RTM, we put forth a multi-graphic processing unit (GPU) calculative strategy, i.e., by distributing image computational domains to different GPUs for computation and by using the method of multi-stream calculations to conceal data transmission between GPUs. Ultimately, the computing original efficiency was higher relative to a single GPU, and more GPUs were used linearly. The test with a model showed that the attributive migration methods can correctly output SOGs, while the GPU parallel computation can effectively improve the computing efficiency. Therefore, it is of practical importance for this method to be expanded and applied in industries.

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3D hybrid-domain full waveform inversion on GPU

Cited by 38 publications

References 28 publications

An efficient elastic full‐waveform inversion of multiple parameters with ocean‐bottom seismometer data

An efficient elastic full‐waveform inversion of multiple parameters with ocean‐bottom seismometer data

Numerical comparison of Rayleigh and Love full waveform inversion in characterizing soil spatial variability for near‐surface engineering applications

Calculation of Surface Offset Gathers Based on Reverse Time Migration and Its Parallel Computation with Multi-GPUs

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