4 citations · 8 across the 4 of their papers we have counts for
4 papers
Generative Diffusion Model for Seismic Imaging Improvement of Sparsely Acquired Data and Uncertainty Quantification
Xingchen Shi, Shijun Cheng, Weijian Mao +1
Seismic imaging from sparsely acquired data faces challenges such as low image quality, discontinuities, and migration swing artifacts. Existing convolutional neural network (CNN)-…
Discovery of physically interpretable wave equations
Shijun Cheng, Tariq Alkhalifah
Using symbolic regression to discover physical laws from observed data is an emerging field. In previous work, we combined genetic algorithm (GA) and machine learning to present a…
Meta-PINN: Meta learning for improved neural network wavefield solutions
Shijun Cheng, Tariq Alkhalifah
Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised fea…
An effective self-supervised learning method for various seismic noise attenuation
Shijun Cheng, Zhiyao Cheng, Chao Jiang +2
Faced with the scarcity of clean label data in real scenarios, seismic denoising methods based on supervised learning (SL) often encounter performance limitations. Specifically, wh…