Full waveform inversion (FWI) aims to reconstruct subsurface velocity models from observed seismic wavefields and has recently benefited from advances in deep learning (DL). The performance of DL-based FWI critically depends on the diversity of training data, yet existing datasets such as OpenFWI rely on fixed or weakly varying source conditions, limiting their ability to represent realistic seismic scenarios and hindering source generalization. To address this issue, we construct a new source-variable seismic dataset, termed SVFWI, by systematically varying the frequencies and horizontal locations of multiple surface sources. SVFWI is further divided into three subsets that respectively model frequency variations, location variations, and their combined effects, providing a challenging benchmark in data-driven FWI. We further propose SG-DeepONet, a novel DeepONet-based encoder-decoder framework tailored for FWI. The branch network extracts multi-scale time-frequency features from seismic observations, the trunk network explicitly embeds source physical parameters, and an interactive decoding network enables effective nonlinear fusion and high-fidelity velocity reconstruction. Extensive experiments on SVFWI demonstrate that SG-DeepONet achieves superior inversion accuracy and robustness under varying source conditions compared with existing DL-based FWI methods.
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器科学与电气工程学院）；Northeast Electric Power University（东北电力大学）；College of Communication Engineering, Jilin University（吉林大学通信工程学院）
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；The Pennsylvania State University（宾夕法尼亚州立大学）；Michigan State University（密歇根州立大学）；Microsoft Research（微软研究院）
This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velocity map is extremely expensive, making it impractical to scale up a supervised approach to train the mapping from seismic data to velocity maps with convolutional neural networks (CNN). We address these difficulties by integrating PDE and CNN in a loop, thus shifting the paradig…
Full waveform inversion method based on diffusion model
School of Information and Mathematics, Yangtze University（信息与数学学院，长江大学）；School of Electronic Information and Electrical Engineering, Yangtze University（电子信息与电气工程学院，长江大学）
A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University（数学与应用数学及统计学系，凯斯西储大学）；School of AI and Advanced Computing, Xi’an Jiaotong Liverpool University（人工智能与先进计算学院，西安交通大学利物浦大学）
Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion
School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学科学学院）；CMA-Shanghai, Shanghai Jiao Tong University（上海交通大学CMA-上海）；School of Mathematics and Statistics, Lanzhou University（兰州大学数学与统计学院）
SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion
China University of Mining and Technology - Beijing（中国矿业大学（北京））；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Improving Full Waveform Inversion in Large Model Era
School of Data Science and Society（数据科学与社会学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Pennsylvania State University（宾夕法尼亚州立大学）；Google DeepMind（谷歌DeepMind）
Ambient Noise Full Waveform Inversion with Neural Operators
Seismological Laboratory, California Institute of Technology（加州理工学院地震实验室）；Department of Geology and Geophysics, University of Utah（犹他大学地质与地球物理系）；Nvidia Corporation（英伟达公司）
Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3D high-resolution velocity maps via deep networks is still a great challenge. In this paper, we present InversionNet3D, an efficient and scalable encoder-decoder network for 3D FWI. The proposed method employs group convolution in the encoder to establish an e…
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Colorado School of Mines（科罗拉多矿业学院）
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI problem can be challenging due to its ill-posedness and high computational cost. In this work, we develop a new hybrid computational approach to solve FWI that combines physics-based models with data-driven methodologies. In particular, we develop a data augmentation strategy that can not only improve the representativity of the training set but also incorporate important governing physics into the training process and th…
The data-driven approach has been demonstrated as a promising technique to solve complicated scientific problems. Full Waveform Inversion (FWI) is commonly epitomized as an image-to-image translation task, which motivates the use of deep neural networks as an end-to-end solution. Despite being trained with synthetic data, the deep learning-driven FWI is expected to perform well when evaluated with sufficient real-world data. In this paper, we study such properties by asking: how robust are these deep neural networks and how do they generalize? For robustness, we prove the upper bounds of the d…
RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion
Department of Statistics and Data Science, Yale University（耶鲁大学统计与数据科学系）；School of Data Science and Society, University of North Carolina at Chapel Hill（北卡罗来纳大学夏洛特分校数据科学与社会学院）
SA-EMO: Structure-Aligned Encoder Mixture of Operators for Generalizable Full-waveform Inversion
School of Science, China University of Mining and Technology, Beijing（科学学院，中国矿业大学（北京））；School of Artificial Intelligence, China University of Mining and Technology, Beijing（人工智能学院，中国矿业大学（北京））；City University of Hong Kong (Dongguan)（香港城市大学（东莞））
Tensor Train Decomposition-based 3D Implicit Full Waveform Inversion with Multi-scale Structural Similarity
State Key Laboratory of Deep Earth Exploration and Imaging（深部地球探测与成像国家重点实验室）；College of GeoExploration Science and Technology, Jilin University（吉林大学地球探测科学与技术学院）；Petroleum Exploration and Production Research Institute, Sinopec（中石油石油勘探生产研究院）
Universidade Federal do Espírito Santo（巴西联邦圣埃斯皮里图大学）；Instituto Federal do Espírito Santo（圣埃斯皮里图联邦理工学院）；Exploração e Produção Petróleo Brasileiro S.A.（巴西国家石油公司勘探与生产部门）
In the Oil and Gas industry, estimating a subsurface velocity field is an essential step in seismic processing, reservoir characterization, and hydrocarbon volume calculation. Full-waveform inversion (FWI) velocity modeling is an iterative advanced technique that provides an accurate and detailed velocity field model, although at a very high computational cost due to the physics-based numerical simulations required at each FWI iteration. In this study, we propose a method of generating velocity field models, as detailed as those obtained through FWI, using a conditional generative adversarial …
Baidu Silicon Valley Artificial Intelligence Lab（百度硅谷人工智能实验室）
We propose the multi-head convolutional neural network (MCNN) architecture for waveform synthesis from spectrograms. Nonlinear interpolation in MCNN is employed with transposed convolution layers in parallel heads. MCNN achieves more than an order of magnitude higher compute intensity than commonly-used iterative algorithms like Griffin-Lim, yielding efficient utilization for modern multi-core processors, and very fast (more than 300x real-time) waveform synthesis. For training of MCNN, we use a large-scale speech recognition dataset and losses defined on waveforms that are related to perceptu…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Most of the available advanced misfit functions for full waveform inversion (FWI) are hand-crafted, and the performance of those misfit functions is data-dependent. Thus, we propose to learn a misfit function for FWI, entitled ML-misfit, based on machine learning. Inspired by the optimal transport of the matching filter misfit, we design a neural network (NN) architecture for the misfit function in a form similar to comparing the mean and variance for two distributions. To guarantee the resulting learned misfit is a metric, we accommodate the symmetry of the misfit with respect to its input an…
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Department of Geophysics, Colorado School of Mines（地质学系，科罗拉多矿业学院）；Department of Energy Science and Engineering, Stanford University（能源科学与工程系，斯坦福大学）；Department of Petroleum Engineering, Colorado School of Mines（石油工程系，科罗拉多矿业学院）
Center for Energy and Geo Processing (CeGP) at Georgia Tech and KFUPM（乔治亚理工学院与法赫德国国石油矿物大学能源与地球处理中心）
Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between the recorded and calculated seismic data. It has been attacked successfully with the Gauss-Newton method and sparsity promoting regularization based on fixed multiscale transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical transforms with predefined dictionaries to achieve sparse representation, we introduce an adaptive tra…
