3 papers
physics.geo-ph2026
A Review of Deep-learning-based Seismic Data Denoising and Its Promising Paradigm Shift to Foundation Models
Xintong Dong, Zhengyi Yuan, Changxin Wei +3
Denoising is a long-standing and widely-concerned topic in seismic data processing, for it can significantly increase the signal-to-noise ratio of seismic data. Numerous deep learn…
cs.LG2026
Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
Zhengyi Yuan, Xintong Dong, Xinyang Wang +2
In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension.…
physics.geo-ph2025
PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency
Xintong Dong, Zhengyi Yuan, Jun Lin +3
Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observ…