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Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem.
Absorbing boundary conditions for numerical simulation of waves
Björn Engquist and Andrew Majda · 1977
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Source-independent full-waveform inversion of seismic data
Ki Ha Lee and Hee Joon Kim · 2003
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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The finite-difference time-domain method for modeling of seismic wave propagation
Peter Moczo, Johan OA Robertsson, and Leo Eisner · 2007
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An overview of full-waveform inversion in exploration geophysics
Jean Virieux and Stéphane Operto · 2009
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Source wavelet estimation in full waveform inversion
Dong Sun, Kun Jiao, D Vigh, and R Coates · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow · 2016
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Deep-learning tomography
Mauricio Araya-Polo, Joseph Jennings, Amir Adler, and Taylor Dahlke · 2018
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InversionNet: An efficient and accurate data-driven full waveform inversion
Yue Wu and Youzuo Lin · 2019
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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Robust learning with Jacobian regularization
Judy Hoffman, Daniel A Roberts, and Sho Yaida · 2019
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FWI Imaging: Full-wavefield imaging through full-waveform inversion
Zhigang Zhang, Zedong Wu, Zhiyuan Wei, Jiawei Mei, Rongxin Huang, and Ping Wang · 2020
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A theory-guided deep-learning formulation and optimization of seismic waveform inversion theory-guided dl and seismic inversion
Jian Sun, Zhan Niu, Kristopher A Innanen, Junxiao Li, and Daniel O Trad · 2020
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Data-driven seismic waveform inversion: A study on the robustness and generalization
Zhongping Zhang and Youzuo Lin · 2020
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Deep learning-based artificial bandwidth extension: Training on ultrasparse obn to enhance towed-streamer FWI
Mehdi Aharchaou and Anatoly Baumstein · 2020
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A physics-based neural-network way to perform seismic full waveform inversion
Yuxiao Ren, Xinji Xu, Senlin Yang, Lichao Nie, and Yangkang Chen · 2020
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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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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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Generalizing universal function approximators
Irina Higgins · 2021
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Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
Sifan Wang, Hanwen Wang, and Paris Perdikaris · 2021
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Seismic velocity model building using neural networks: Training data design and learning generalization
Hani Alzahrani and Jeffrey Shragge · 2022
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Deep learning with adaptive attention for seismic velocity inversion
Fangda Li, Zhenwei Guo, Xinpeng Pan, Jianxin Liu, Yanyi Wang, and Dawei Gao · 2022
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Elastic-adjointnet: A physics-guided deep autoencoder to overcome crosstalk effects in multiparameter full-waveform inversion
Arnab Dhara and Mrinal Sen · 2022
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Deep learning for geophysics: Current and future trends
Siwei Yu and Jianwei Ma · 2021
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Deep-learning seismic full-waveform inversion for realistic structural models
Bin Liu, Senlin Yang, Yuxiao Ren, Xinji Xu, Peng Jiang, and Yangkang Chen · 2021
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Multiscale data-driven seismic full-waveform inversion with field data study
Shihang Feng, Youzuo Lin, and Brendt Wohlberg · 2021
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Physics-informed machine learning
George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Operator learning for predicting multiscale bubble growth dynamics
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Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantificationnnfwi
Weiqiang Zhu, Kailai Xu, Eric Darve, Biondo Biondi, and Gregory C Beroza · 2022
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MIONet: Learning multiple-input operators via tensor product
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Approximation rates of deeponets for learning operators arising from advection–diffusion equations
Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, and George Em Karniadakis · 2022
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U-FNO—an enhanced Fourier neural operator-based deep-learning model for multiphase flow
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, and Sally M Benson · 2022
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Physics-guided data-driven seismic inversion: Recent progress and future opportunities in full-waveform inversion
Youzuo Lin, James Theiler, and Brendt Wohlberg · 2023
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Neural operator prediction of linear instability waves in high-speed boundary layers
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Reliable extrapolation of deep neural operators informed by physics or sparse observations
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