Multisource Reverse-time Migration and Full-waveform Inversion on a GPGPU

Boonyasiriwat, Chaiwoot; Zhan, Ge; Hadwiger, Markus; Srinivasan, Madhusudhanan; Schuster, Gerard T.

doi:10.3997/2214-4609.201401241

Cited by 4 publications

(2 citation statements)

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“…Recent advances in computing capability and hardware makes FWI a popular research subject to improve velocity models. As a booming technology, graphics processing unit (GPU) has been widely used to mitigate the computational drawbacks in seismic imaging (Micikevicius, 2009;Yang et al, 2014) and inversion (Boonyasiriwat et al, 2010;Shin et al, 2014), due to its potential gain in performance. One key problem for GPU implementation is that the parallel computation is much faster while the data communication between host and device always takes longer time.…”

Section: Introductionmentioning

confidence: 99%

A graphics processing unit implementation of time-domain full-waveform inversion

2015

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The graphics processing unit (GPU) has become a popular device for seismic imaging and inversion due to its superior speedup performance. In this paper we implement GPU-based full waveform inversion (FWI) using the wavefield reconstruction strategy. Because the computation on GPU is much faster than CPU-GPU data communication, in our implementation the boundaries of the forward modeling are saved on the device to avert the issue of data transfer between host and device. The Clayton-Enquist absorbing boundary is adopted to maintain the efficiency of GPU computation. A hybrid nonlinear conjugate gradient algorithm combined with the parallel reduction scheme is utilized to do computation in GPU blocks. The numerical results confirm the validity of our implementation.

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Section: Introductionmentioning

confidence: 99%

A graphics processing unit implementation of time-domain full-waveform inversion

2015

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“…The computational kernel is responsible for simulating seismic wavefields with the purpose of measuring data misfits, generating gradients and obtaining approximations to the Hessian. Some acceleration efforts focusing on the FWI computing kernels include porting these kernels to accelerator-based hardware [5,11,39], optimizing the kernels' performance in general purpose processors [23] for off-the-shelf hardware or parallelizing the kernels in many compute units [30,36,38]. Nevertheless, we wish to show here that there are workflow strategies that are orthogonal to kernel optimization and which result in notable computational savings, thus making elastic FWI a routinely applicable tool for 3D datasets.…”

Section: Introductionmentioning

confidence: 99%

Acceleration strategies for elastic full waveform inversion workflows in 2D and 3D

et al. 2016

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Full waveform inversion (FWI) is one of the most challenging procedures to obtain quantitative information of the subsurface. For elastic inversions, when both compressional and shear velocities have to be inverted, the algorithmic issue becomes also a computational challenge due to the high cost related to modelling elastic rather than acoustic waves. This shortcoming has been moderately mitigated by using high-performance computing to accelerate 3D elastic FWI kernels. Nevertheless, there is room in the FWI workflows for obtaining large speedups at the cost of proper grid pre-processing and data decimation techniques. In the present work, we show how by making full use of frequency-adapted grids, composite shot lists and a novel dynamic offset control strategy, we can reduce by several orders of magnitude the compute time while improving the convergence of the method in the studied cases, regardless of the forward and adjoint compute kernels used.

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