2 citations · 2 across the 4 of their papers we have counts for
4 papers
Propagating the prior from shallow to deep with a pre-trained velocity-model Generative Transformer network
Randy Harsuko, Shijun Cheng, Tariq Alkhalifah
Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learnin…
A self-supervised learning framework for seismic low-frequency extrapolation
Shijun Cheng, Yi Wang, Qingchen Zhang +2
Full waveform inversion (FWI) is capable of generating high-resolution subsurface parameter models, but it is susceptible to cycle-skipping when the data lack low-frequency. Unfort…
Robust data driven discovery of a seismic wave equation
Shijun Cheng, Tariq Alkhalifah
Despite the fact that our physical observations can often be described by derived physical laws, such as the wave equation, in many cases, we observe data that do not match the law…
Gabor-based learnable sparse representation for self-supervised denoising
Sixiu Liu, Shijun Cheng, Tariq Alkhalifah
Traditional supervised denoising networks learn network weights through "black box" (pixel-oriented) training, which requires clean training labels. The uninterpretability nature o…