4 citations · 6 across the 7 of their papers we have counts for
8 papers
Incorporating wave physical priors into diffusion models: A novel approach to seismic resolution enhancement
Huanhuan Tang, Shijun Cheng, Weijian Mao +2
Seismic resolution enhancement remains a critical challenge in exploration geophysics, particularly when processing field data characterized by limited bandwidth, strong noise, and…
Adaptive Self-Supervised Surface-Related Multiple Suppression
Huan Song, Shijun Cheng, Huanhuan Tang +2
Effective suppression of surface-related multiples is essential to prevent imaging artifacts and erroneous structural interpretations. While conventional approaches rely on accurat…
Inhomogeneous plane waves in attenuative anisotropic porous media
Lingli Gao, Weijian Mao, Qianru Xu +3
We investigate the propagation of inhomogeneous plane waves in poro-viscoelastic media, explicitly incorporating both velocity and attenuation anisotropy. Starting from classical B…
Generating Reliable Initial Velocity Models for Full-waveform Inversion with Well and Structural Constraints
Qingchen Zhang, Shijun Cheng, Wei Chen +1
Full waveform inversion (FWI) plays an important role in velocity modeling due to its high-resolution advantages. However, its highly non-linear characteristic leads to numerous lo…
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)-…
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…
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…
Meta-Processing: A robust framework for multi-tasks seismic processing
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Machine learning-based seismic processing models are typically trained separately to perform specific seismic processing tasks (SPTs), and as a result, require plenty of training d…