Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations

Technical Report · Mon Sep 18 04:00:00 EDT 2023

DOI:https://doi.org/10.2172/2001189· OSTI ID:2001189

Kong, Qingkai ^[1]; Rodgers, A. ^[1]

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1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC52-07NA27344
• OSTI ID: 2001189
• Report Number(s): LLNL--TR-854521; 1082411
• Country of Publication: United States
• Language: English

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58 GEOSCIENCES