Abstract
Pengyuan Sun, Puyi Hou, Ye Lin
Abstract
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Operator approximation of the wave equation based on deep learning of green’s function
2024
Unresolved referenced work
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A nonreflecting boundary condition for discrete acoustic and elastic wave equations
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Correlation-based reflection waveform inversion by one-way wave equations
10.1111/1365-2478.12668 · 2018
Adaptive Fourier neural operators: efficient token mixers for transformers
2022
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Full-waveform inversion, part 1: forward modeling
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
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PyTorch: an imperative style, high-performance deep learning library
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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · 2019
P-SV wave propagation in heterogeneous media: velocity-stress finite-difference method
10.1190/1.1442147 · 1986
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Elastic isotropic and anisotropic full waveform inversions using automatic differentiation for gradient calculations in a framework of recurrent neural networks
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Full waveform inversion guided wave tomography with a recurrent neural network
10.1016/j.ultras.2023.107043 · 2023
Respecting causality for training physics-informed neural networks
2024
U-FNO — an enhanced Fourier neural operator-based deep-learning model for multiphase flow
10.1016/j.advwatres.2022.104180 · 2022
InversionNet: accurate and efficient seismic waveform inversion with convolutional neural networks
2018
Seismic wave propagation and inversion with neural operators
10.1785/0320210026 · 2021
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
2023
Seismic wave propagation and inversion with neural operators
10.1785/0320210026 · doi-reference
U-FNO — an enhanced Fourier neural operator-based deep-learning model for multiphase flow
10.1016/j.advwatres.2022.104180 · doi-reference
Full waveform inversion guided wave tomography with a recurrent neural network
10.1016/j.ultras.2023.107043 · doi-reference
Elastic isotropic and anisotropic full waveform inversions using automatic differentiation for gradient calculations in a framework of recurrent neural networks
10.1190/geo2020-0542.1 · doi-reference
An overview of full-waveform inversion in exploration geophysics
10.1190/1.3238367 · doi-reference
P-SV wave propagation in heterogeneous media: velocity-stress finite-difference method
10.1190/1.1442147 · doi-reference
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · doi-reference
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
10.1038/s42256-021-00302-5 · doi-reference
Full-waveform inversion, part 2: adjoint modeling
10.1190/tle37010069.1 · doi-reference
Full-waveform inversion, part 1: forward modeling
10.1190/tle36121033.1 · doi-reference
Fourth-order finite-difference P-SV seismograms
10.1190/1.1442422 · doi-reference
Forward modeling by a Fourier method
10.1190/1.1441288 · doi-reference
Introduction to the spectral element method for three-dimensional seismic wave propagation
10.1046/j.1365-246x.1999.00967.x · doi-reference
Correlation-based reflection waveform inversion by one-way wave equations
10.1111/1365-2478.12668 · doi-reference
A nonreflecting boundary condition for discrete acoustic and elastic wave equations
10.1190/1.1441945 · doi-reference