121 citations · 206 across the 11 of their papers we have counts for
7 papers · 1 filter
SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents
Yingjie Xu, Siwei Yu, Jianwei Ma
Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger than manual trial and error ca…
Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition
Yanqi Wu, Hossein S. Aghamiry, Stephane Operto +1
Frequency-domain simulation of seismic waves plays an important role in seismic inversion, but it remains challenging in large models. The recently proposed physics-informed neural…
CNN-tuned spatial filters for P- and S-wave decomposition and applications in elastic imaging
Wenlong Wang, Jianwei Ma, George A. McMechan
P- and S-wave decomposition is essential for imaging multi-component seismic data in elastic media. A data-driven workflow is proposed to obtain a set of spatial filters that are h…
Data-driven geophysics: from dictionary learning to deep learning
Siwei Yu, Jianwei Ma
Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time;…
Deep-learning inversion: a next generation seismic velocity-model building method
Fangshu Yang, Jianwei Ma
Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are key prerequisites for reverse-time migration and other high-resol…
Can learning from natural image denoising be used for seismic data interpolation?
Hao Zhang, Xiuyan Yang, Jianwei Ma
We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation. It provides a simple and efficient way to break though the lack problem of ge…
Deep learning for denoising
Siwei Yu, Jianwei Ma, Wenlong Wang
Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is…