Fourier Neural Operator Surrogate Model to Predict 3d Seismic Waves Propagation

Lehmann, Fanny; Gatti, Filippo; Bertin, Michaël; Clouteau, Didier

doi:10.7712/120223.10339.20362

Cited by 4 publications

(1 citation statement)

References 30 publications

(33 reference statements)

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“…Predictions of surface wavefields were also conducted with Fourier Neural Operators based on the HEMEW-3D database (Lehmann et al, 2023a). This SciML method takes as inputs 3D geological models and returns 3D velocity fields (functions of two spatial coordinates locating the sensor and a third dimension for time).…”

Section: Applicationsmentioning

confidence: 99%

Synthetic ground motions in heterogeneous geologies: the HEMEW-3D dataset for scientific machine learning

Lehmann,

Gatti,

Bertin

et al. 2024

Preprint

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Abstract. The ever-improving performances of physics-based simulations and the rapid developments of deep learning are offering new perspectives to study earthquake-induced ground motion. Due to the large amount of data required to train deep neural networks, applications have so far been limited to recorded data or two-dimensional simulations. To bridge the gap between deep learning and high-fidelity numerical simulations, this work introduces a new database of physics-based earthquake simulations. The HEMEW-3D database comprises 30,000 simulations of elastic wave propagation in three-dimensional (3D) geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. For each simulation, ground motion is synthetized at the surface by a grid of virtual sensors. The high frequency of waveforms (fmax = 5 Hz) allows extensive analyses of surface ground motion. Existing and foreseen applications range from statistic analyses of the ground motion variability and machine learning methods on geological models, to deep learning-based predictions of ground motion depending on 3D heterogeneous geologies.

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Section: Applicationsmentioning

confidence: 99%

Synthetic ground motions in heterogeneous geologies: the HEMEW-3D dataset for scientific machine learning

Lehmann,

Gatti,

Bertin

et al. 2024

Preprint

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Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator

Peng

Yuan

et al. 2023

Physics of Fluids

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Long-term predictions of nonlinear dynamics of three-dimensional (3D) turbulence are very challenging for machine learning approaches. In this paper, we propose an implicit U-Net enhanced Fourier neural operator (IU-FNO) for stable and efficient predictions on the long-term large-scale dynamics of turbulence. The IU-FNO model employs implicit recurrent Fourier layers for deeper network extension and incorporates the U-net network for the accurate prediction on small-scale flow structures. The model is systematically tested in large-eddy simulations of three types of 3D turbulence, including forced homogeneous isotropic turbulence, temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The numerical simulations demonstrate that the IU-FNO model is more accurate than other FNO-based models, including vanilla FNO, implicit FNO (IFNO), and U-Net enhanced FNO (U-FNO), and dynamic Smagorinsky model (DSM) in predicting a variety of statistics, including the velocity spectrum, probability density functions of vorticity and velocity increments, and instantaneous spatial structures of flow field. Moreover, IU-FNO improves long-term stable predictions, which has not been achieved by the previous versions of FNO. Moreover, the proposed model is much faster than traditional large-eddy simulation with the DSM model and can be well generalized to the situations of higher Taylor–Reynolds numbers and unseen flow regime of decaying turbulence.

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Fourier neural operator for large eddy simulation of compressible Rayleigh–Taylor turbulence

Luo,

Li,

Yuan

et al. 2024

Physics of Fluids

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The Fourier neural operator (FNO) framework is applied to the large eddy simulation (LES) of three-dimensional compressible Rayleigh–Taylor turbulence with miscible fluids at Atwood number At=0.5, stratification parameter Sr = 1.0, and Reynolds numbers Re = 10 000 and 30 000. The FNO model is first used for predicting three-dimensional compressible turbulence. The different magnitudes of physical fields are normalized using root mean square values for an easier training of FNO models. In the a posteriori tests, the FNO model outperforms the velocity gradient model, the dynamic Smagorinsky model, and implicit large eddy simulation in predicting various statistical quantities and instantaneous structures, and is particularly superior to traditional LES methods in predicting temperature fields and velocity divergence. Moreover, the computational efficiency of the FNO model is much higher than that of traditional LES methods. FNO models trained with short-time, low Reynolds number data exhibit a good generalization performance on longer-time predictions and higher Reynolds numbers in the a posteriori tests.

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Fourier Neural Operator Surrogate Model to Predict 3d Seismic Waves Propagation

Cited by 4 publications

References 30 publications

Synthetic ground motions in heterogeneous geologies: the HEMEW-3D dataset for scientific machine learning

Synthetic ground motions in heterogeneous geologies: the HEMEW-3D dataset for scientific machine learning

Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator

Fourier neural operator for large eddy simulation of compressible Rayleigh–Taylor turbulence

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