Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator

Journal Article · Thu Nov 16 00:00:00 EST 2023 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/tgrs.2023.3333663· OSTI ID:2507005

Li, Bian ^[1]; Wang, Hanchen ^[2]; Feng, Shihang ^[2]; Yang, Xiu ^[3]; Lin, Youzuo ^[4]

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1. Lehigh University, Bethlehem, PA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
3. Lehigh University, Bethlehem, PA (United States)
4. University of North Carolina, Chapel Hill, NC (United States)

Here, in the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial differential equations (PDEs), including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice. Instead, inspired by the idea of operator learning, this work leverages the Fourier neural operator (FNO) to effectively learn the frequency domain seismic wavefields under the context of variable velocity models. We also propose a new framework paralleled FNO (PFNO) for efficiently training the FNO-based solver given multiple source locations and frequencies. Numerical experiments demonstrate the high accuracy of both FNO and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore, the cross-dataset generalization test verifies that PFNO adapts to out-of-distribution velocity models. Finally, PFNO admits higher computational efficiency on large-scale testing datasets than the traditional finite-difference method. The aforementioned advantages endow the FNO-based solver with the potential to build powerful models for research on seismic waves.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Carbon Management
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2507005
• Report Number(s): LA-UR--22-30147
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 61; ISSN 0196-2892
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
Earth Sciences
Fourier neural operator
Helmholtz equation
operator learning
seismic wave