deep learning 1dispersion inversion 1mode separation 1physics-informed neural networks 1surface-wave analysis 1
From the 1 of 3 linked papers with an AI index.
3 papers
physics.geo-ph2026
Dispersion-Guided Physics-Aware Deep Inverse Operator for Surface Wave Mode Separation
Yang Cui, Sujith Swaminadhan, Yangkang Chen +2
The paper presents an unsupervised physics-aware deep learning framework that separates fundamental and higher surface‑wave modes directly in the time‑space domain by using adaptiv…
physics.geo-ph2026
Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising
Jiahua Zhao, Umair bin Waheed, Jing Sun +3
The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distri…
physics.geo-ph2025
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…