Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Cheng, Shijun"'
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 learning, these velocity models (or, more precisely, their distribution) can
Externí odkaz:
http://arxiv.org/abs/2408.09767
Seismic imaging from sparsely acquired data faces challenges such as low image quality, discontinuities, and migration swing artifacts. Existing convolutional neural network (CNN)-based methods struggle with complex feature distributions and cannot e
Externí odkaz:
http://arxiv.org/abs/2407.21683
Autor:
Cheng, Shijun, Alkhalifah, Tariq
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 data-driven method for discovering a wave equation. Although it manage
Externí odkaz:
http://arxiv.org/abs/2404.17971
Autor:
Cheng, Shijun, Alkhalifah, Tariq
Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applic
Externí odkaz:
http://arxiv.org/abs/2401.11502
Faced with the scarcity of clean label data in real scenarios, seismic denoising methods based on supervised learning (SL) often encounter performance limitations. Specifically, when a model trained on synthetic data is directly applied to field data
Externí odkaz:
http://arxiv.org/abs/2311.02193
Autor:
Cheng, Shijun, Alkhalifah, Tariq
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 laws or have not been described physically yet. Therefore recently, a bra
Externí odkaz:
http://arxiv.org/abs/2309.13645
Traditional supervised denoising networks learn network weights through "black box" (pixel-oriented) training, which requires clean training labels. The uninterpretability nature of such denoising networks in addition to the requirement for clean dat
Externí odkaz:
http://arxiv.org/abs/2308.03077
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 data. However, preparing training data sets is not trivial, especially
Externí odkaz:
http://arxiv.org/abs/2307.14851
Autor:
Cao, Yi1 (AUTHOR), Cheng, Shijun2 (AUTHOR), Tucker, Jennifer Wu3 (AUTHOR) jenny.tucker@warrington.ufl.edu, Wan, Chi4 (AUTHOR)
Publikováno v:
Contemporary Accounting Research. Fall2023, Vol. 40 Issue 3, p2106-2139. 34p.
Publikováno v:
In Journal of Applied Geophysics September 2023 216