Elastic full-waveform inversion based on GPU accelerated temporal fourth-order finite-difference approximation

Fang, Jinwei; Chen, Hanming; Zhou, Hui; Rao, Ying; Sun, Peng; Zhang, Jianlei

doi:10.1016/j.cageo.2019.104381

Cited by 21 publications

(5 citation statements)

References 43 publications

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“…Therefore, our proposed method can replace the FWI modeling or providing an initial velocity model that will need fewer iterations to converge during traditional FWI, similar strategy adopted by [36] A fair comparison between the FWI and our cGAN surrogate model should consider the implementation of the FWI algorithm in parallel, using GPU hardware. Although some papers have proposed GPU implementations of the FWI algorithm [37], [38] and [39], they only used 2D synthetic data, mainly due to GPU memory constraints. At present, FWI algorithm processing on GPU hardware has yet to become industrially feasible.…”

Section: Discussionmentioning

confidence: 99%

Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks

Saraiva

Forechi

Neto

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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In the Oil and Gas industry, estimating a subsurface velocity field is an essential step in seismic processing, reservoir characterization, and hydrocarbon volume calculation. Full-waveform inversion (FWI) velocity modeling is an iterative advanced technique that provides an accurate and detailed velocity field model, although at a very high computational cost due to the physics-based numerical simulations required at each FWI iteration. In this study, we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial network (cGAN) with multiple inputs. The primary motivation of this approach is to circumvent the extremely high cost of full-waveform inversion velocity modeling. Real-world data were used to train and test the proposed network architecture, and three evaluation metrics (percent error, structural similarity index measure, and visual analysis) were adopted as quality criteria. Based on these metrics, the results evaluated upon the test set suggest that the GAN was able to accurately match real FWI generated outputs, enabling it to extract from input data the main geological structures and lateral velocity variations. Experimental results indicate that the proposed method, when deployed, has the potential to increase the speed of geophysical reservoir characterization processes, saving on time and computational resources.

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Section: Discussionmentioning

confidence: 99%

Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks

Saraiva

Forechi

Neto

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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“…The partial differential terms are substituted with algebraic differences for a small argument x, such that x → 0, which makes it easy to implement and understand (refer Equations ( 6) and ( 7)). The finite difference formulation for seismic waves can be found in: (a) [18][19][20] for forward wave propagation problems and (b) [12,[21][22][23] for FWI problems. To satisfy Sommerfeld's radiation condition at infinity (i.e., the wave radiating towards infinity should not come back unless there are reflecting boundaries), absorbing boundaries or perfectly matched layers (PML) [12] is used.…”

Section: The Seismic Full Waveform Inversionmentioning

confidence: 99%

Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization

et al. 2022

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Full waveform inversion is a widely used technique to estimate the subsurface parameters with the help of seismic measurements on the surface. Due to the amount of data, model size and non-linear iterative procedures, the numerical computation of Full Waveform Inversion are computationally intensive and time-consuming. This paper addresses the parallel computation of seismic full waveform inversion with Graphical Processing Units. Seismic full-waveform inversion of in-plane wave propagation in the finite difference method is presented here. The stress velocity formulation of the wave equation in the time domain is used. A four nodded staggered grid finite-difference method is applied to solve the equation, and the perfectly matched layers are considered to satisfy Sommerfeld’s radiation condition at infinity. The gradient descent method with conjugate gradient method is used for adjoined modelling in full-waveform inversion. The host code is written in C++, and parallel computation codes are written in CUDA C. The computational time and performance gained from CUDA C and OpenMP parallel computation in different hardware are compared to the serial code. The performance improvement is enhanced with increased model dimensions and remains almost constant after a certain threshold. A GPU performance gain of up to 90 times is obtained compared to the serial code.

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“…Though full-waveform inversion using SGFD with the highorder accuracy in time has been proposed (Fang et al, 2020;Ren et al, 2021), 3D ERTM with a temporal high-order SGFD method has not been reported. We take our previous numerical simulation and migration imaging work (Chen et al, 2016b;Fang et al, 2022a) a step further toward the 3D ERTM workflow, improving parts such as source and receiver wavefield simulation, wavefield reconstruction, and MPI parallelism using GPUs (Fang et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional elastic reverse-time migration using a high-order temporal and spatial staggered-grid finite-difference scheme

Fang

Huang²,

Shi³

et al. 2023

Front. Earth Sci.

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Three-dimensional (3D) elastic reverse-time migration (ERTM) can image the subsurface 3D seismic structures, and it is an important tool for the Earth’s interior imaging. A common simulation kernel used in 3D ERTM is the current staggered-grid finite-difference (SGFD) method of the first-order elastic wave equation. However, the mere second-order accuracy in time of the current SGFD method can bring non-negligible time dispersion, which reduces the simulation accuracy and further leads to the distortion of the imaging results. This paper proposes a vector-based 3D ERTM using the high-order accuracy SGFD method in time to obtain high-accuracy images. This approach is a new high-resolution ERTM workflow that improves the imaging accuracy of conventional ERTM from numerical simulation. The proposed ERTM workflow is established on a quasi-stress–velocity wave equation and its vector wavefield decomposition form. Advanced SGFD schemes and their corresponding coefficients with fourth-order temporal accuracy solve the quasi-linear wave equation system. The normalized dot product imaging condition produces high-quality images using high-accuracy vector wavefields solved using the SGFD method. Through the numerical examples, we test the simulation efficiency and analyze how temporal accuracy in numerical simulations affects migration imaging quality. We include that the proposed method obtains highly accurate images.

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Elastic full-waveform inversion based on GPU accelerated temporal fourth-order finite-difference approximation

Cited by 21 publications

References 43 publications

Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks

Data-driven Full-waveform Inversion Surrogate using Conditional Generative Adversarial Networks

Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization

Three-dimensional elastic reverse-time migration using a high-order temporal and spatial staggered-grid finite-difference scheme

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