6 papers
Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent
Chao Li, Mrinal K. Sen, Sergey Fomel +1
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 mod…
Physics-based deep learning for non-stationary adaptive multiple suppression
Chao Li, Yangkang Chen
Surface-related multiple elimination (SRME) is an indispensable processing step in marine seismic exploration, comprising two main components: multiple prediction and adaptive subt…
Dispersion-Guided Physics-Aware Deep Inverse Operator for Surface Wave Mode Separation
Yang Cui, Sujith Swaminadhan, Yangkang Chen +2
Surface-wave (SW) dispersion analysis is widely used in near-surface geophysics and seismology to determine shear-wave velocity structures by measuring SW geometric dispersion in s…
Training a generalizable diffusion model for seismic data processing using a large-scale open-source waveform dataset
Xinyue Gong, Sergey Fomel, Yangkang Chen
We introduce the Seismic Waveforms dataset for Automatic Neural-network processing (SWAN), a comprehensive and standardized benchmark designed to advance data-driven seismic signal…
Spectral Defense Against Resource-Targeting Attack in 3D Gaussian Splatting
Yang Chen, Yi Yu, Jiaming He +3
Recent advances in 3D Gaussian Splatting (3DGS) deliver high-quality rendering, yet the Gaussian representation exposes a new attack surface, the resource-targeting attack. This at…
Learning from Imperfect Labels: A Physics-Aware Neural Operator with Application to DAS Data Denoising
Yang Cui, Denis Anikiev, Umair Bin Waheed +1
Supervised deep learning methods typically require large datasets and high-quality labels to achieve reliable predictions. However, their performance often degrades when trained on…