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In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models.
Inversion of seismic reflection data in the acoustic approximation
Albert Tarantola · 1984
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Marmousi, model and data
A Brougois, Marielle Bourget, Patriek Lailly, Michel Poulet, Patrice Ricarte, and Roelof Versteeg · 1990
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Approximation and estimation bounds for artificial neural networks
Andrew R Barron · 1994
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Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
Tianping Chen and Hong Chen · 1995
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An optimal 9-point, finite-difference, frequency-space, 2-d scalar wave extrapolator
Churl-Hyun Jo, Changsoo Shin, and Jung Hee Suh · 1996
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Numerical solution for high order differential equations using a hybrid neural network—optimization method
Alaeddin Malek and R Shekari Beidokhti · 2006
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Seismic imaging of complex onshore structures by 2d elastic frequency-domain full-waveform inversion
Romain Brossier, Stéphane Operto, and Jean Virieux · 2009
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An overview of full-waveform inversion in exploration geophysics
Jean Virieux and Stéphane Operto · 2009
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Rectified linear units improve restricted Boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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A unified deep artificial neural network approach to partial differential equations in complex geometries
Jens Berg and Kaj Nyström · 2018
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Solving high-dimensional partial differential equations using deep learning
Jiequn Han, Arnulf Jentzen, and E Weinan · 2018
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PDE-net: Learning PDEs from data
Zichao Long, Yiping Lu, Xianzhong Ma, and Bin Dong · 2018
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Microseismic imaging using a source function independent full waveform inversion method
Hanchen Wang and Tariq Alkhalifah · 2018
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Deep learning of subsurface flow via theory-guided neural network
Nanzhe Wang, Dongxiao Zhang, Haibin Chang, and Heng Li · 2020
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Wavefield solutions from machine learned functions constrained by the Helmholtz equation
Tariq Alkhalifah, Chao Song, Umair bin Waheed, and Qi Hao · 2021
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OpenFWI: large-scale multi-structural benchmark datasets for seismic full waveform inversion
Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, and Youzuo Lin · 2021
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Physics-guided deep learning using Fourier neural operators for solving the acoustic VTI wave equation
Tugrul Konuk and Jeffrey Shragge · 2021
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, and George Em Karniadakis · 2021
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Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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InversionNet: an efficient and accurate data-driven full waveform inversion
Yue Wu and Youzuo Lin · 2019
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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Dongkun Zhang, Lu Lu, Ling Guo, and George Em Karniadakis · 2019
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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, and Paris Perdikaris · 2019
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Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020
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Neural operator: Graph kernel network for partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020
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Solving the wave equation with physics-informed deep learning
Ben Moseley, Andrew Markham, and Tarje Nissen-Meyer · 2020
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Wavefield reconstruction inversion via physics-informed neural networks
Chao Song and Tariq Alkhalifah · 2021
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Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks
Chao Song, Tariq Alkhalifah, and Umair Bin Waheed · 2021
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Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis
Jian Sun, Kristopher A Innanen, and Chao Huang · 2021
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Seismic wave propagation and inversion with neural operators
Yan Yang, Angela F Gao, Jorge C Castellanos, Zachary E Ross, Kamyar Azizzadenesheli, and Robert W Clayton · 2021
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Accelerating 2d and 3d frequency-domain seismic wave modeling through interpolating frequency-domain wavefields by deep learning
Wenzhong Cao, Quanli Li, Jie Zhang, and Wei Zhang · 2022
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Unsupervised learning of full-waveform inversion: Connecting CNN and partial differential equation in a loop
Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon Huang, Zicheng Liu, and Youzuo Lin · 2022
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Physics-informed neural networks (PINNs) for wave propagation and full waveform inversions
Majid Rasht-Behesht, Christian Huber, Khemraj Shukla, and George Em Karniadakis · 2022
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High-frequency wavefield extrapolation using the Fourier neural operator
Chao Song and Yanghua Wang · 2022
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