Seismic Imaging

Latest papers

15 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent
Oct 13, 2026Chao Li, Mrinal K. Sen, Sergey Fomel +1Seismic Imaging Bayesian Inverse Problems
The University of Texas at Austin
Full Waveform Inversion with a Learned Preconditioner
Oct 11, 2026Jeongjin ParkSeismic Imaging Inverse Problems
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models
Oct 10, 2026Shijun Cheng, Randy Harsuko, Mohammad H. Taufik +1Seismic Imaging Diffusion Models
Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Self-attention summary networks for subsurface velocity-model building from common-image gathers
Oct 7, 2026Shiqin Zeng, Yunlin Zeng, Abhinav Prakash Gahlot +2Seismic Imaging Inverse Problems
Georgia Institute of Technology
Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion
Sep 11, 2026Chen Min, Haowen Jiang, Zheng Ma +1Physics-Informed Diffusion Models Seismic Imaging
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China · CMA-Shanghai, Shanghai Jiao Tong University, Shanghai, China · School of Mathematics and Statistics, Lanzhou University, Lanzhou, China
Latent Variable-Mediated Cross-Learning for Few-Shot Acoustic Impedance Imaging
Jul 23, 2026Junheng Peng, Yong Li, Mingwei Wang +1Physics-Informed ML Semi-Supervised Learning
The Key Laboratory of Earth Exploration & Information Techniques of Ministry Education, Chengdu University of Technology, Chengdu, 610059, China · School of Resources and Safety Engineering, Chongqing University, Chongqing, 400044, China · Institute of Space Earth Science, Nanjing University, Suzhou, 215163, China
Tensor Train Decomposition-based 3D Implicit Full Waveform Inversion with Multi-scale Structural Similarity
Jun 22, 2026Liangsheng He, Chao Song, Tiansheng Chen +2Implicit Neural Representations 3D Reconstruction
State Key Laboratory of Deep Earth Exploration and Imaging College of GeoExploration Science and Technology, Jilin University Changchun, Jilin, 130021, China · State Key Laboratory of DeepChaoEarthSong Exploration and Imaging College of GeoExploration Science and Technology, Jilin University Changchun, Jilin, 130021, China · Petroleum Exploration and Production Research Institute, Sinopec Beijing, 100083, China
Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network
Jun 19, 2026Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie +1Uncertainty Quantification Bayesian Inverse Problems
Japan Agency for Marine-Earth Science and Technology · Earth Science New Zealand
SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data
May 28, 2026Joseph Stitt, Pratik Rathore, Madeleine Udell +1Synthetic Data Generation PDE Surrogate Modeling
Stanford University
Quantum Feature Pyramid Gating for Seismic Image Segmentation
May 14, 2026Taha Gharaibeh, Jyotsna SharmaImage Segmentation Hybrid Quantum-Classical ML
Louisiana State University Baton Rouge, Louisiana, USA
Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel
May 14, 2026Ruihua Chen, Yisi Luo, Bangyu Wu +2Seismic Imaging Neural Tangent Kernel
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China · School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610000, China
Total Generalized Variation regularization closes the gap between neural-eld and classical methods in seismic travel-time tomography
May 11, 2026Isao KurosawaSeismology PDE Inverse Problems
IVXA, Japan.
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
Mar 31, 2026Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco +2Seismology Conditional Generative Modeling
Georgia Institute of Technology · Oxy · Huber Engineered Materials
SIGMA: A Physics-Based Benchmark for Gas Chimney Understanding in Seismic Images
Mar 24, 2026Bao Truong, Quang Nguyen, Baoru Huang +6Seismic Imaging Image Enhancement
FPT Software AI Center · University of Liverpool · Imperial College London +2
Seismic full-waveform inversion based on a physics-driven generative adversarial network
Mar 16, 2026Xinyi Zhang, Caiyun Liu, Jie Xiong +1Seismic Imaging Inverse Problems
School of Electronic Information and Electrical Engineering, Yangtze University Jingzhou, Hubei 434023, P.R.China

Oct 13, 2026J/K move · Enter open · S save

Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

Chao Li, Mrinal K. Sen, Sergey Fomel, Yangkang Chen

The University of Texas at Austin

Seismic Imaging Bayesian Inverse Problems Inverse Problems Image Inverse Problems Stein Variational Gradient Descent Diffusion-Based Inverse Problems

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Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, especially when the initial model is inaccurate or the acquired data are noisy and incomplete. Annealed Stein variational gradient descent (ASVGD) provides a particle-based Bayesian framework for FWI by approximating the posterior distribution with an ensemble of velocity models, thereby enabling uncertainty quantification beyond a single deterministic solution. However, conventional ASVGD-FWI commonly relies on simple handcrafted priors, such as Gaussian or smoothing assumptions, which are often insufficient to represent complex geological structures. To address this limitation, we introduce a learned diffusion model (DM) as an implicit regularization mechanism within the ASVGD-FWI framework. Instead of explicitly evaluating the diffusion prior density, the trained diffusion model acts as a learned geological projection operator that guides updated particles toward the distribution of plausible velocity models. In this way, the proposed method combines data consistency from FWI, posterior diversity from ASVGD, and structural realism from the diffusion prior. Numerical experiments demonstrate that the diffusion-regularized ASVGD-FWI produces more geologically consistent mean models, suppresses nonphysical artifacts, and yields uncertainty estimates that are more concentrated around structurally complex and poorly constrained regions. These results suggest that learned diffusion priors can effectively enhance Bayesian FWI by improving both inversion stability and the interpretability of posterior uncertainty.