121 citations · 206 across the 11 of their papers we have counts for
13 papers
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…
ORKA: Object reconstruction using a K-approximation graph
Florian Bossmann, Jianwei Ma
Data processing has to deal with many practical difficulties. Data is often corrupted by artifacts or noise and acquiring data can be expensive and difficult. Thus, the given data…
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…
Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning
Fangshu Yang, Jianwei Ma
Seismic full waveform inversion (FWI) is a powerful geophysical imaging technique that produces high-resolution subsurface models by iteratively minimizing the misfit between the s…
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging
Fangshu Yang, Thanh-an Pham, Nathalie Brandenberg +3
Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately,…
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…
Enhanced image approximation using shifted rank-1 reconstruction
Florian Boßmann, Jianwei Ma
Low rank approximation has been extensively studied in the past. It is most suitable to reproduce rectangular like structures in the data. In this work we introduce a generalizatio…
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…
Hartley Spectral Pooling for Deep Learning
Hao Zhang, Jianwei Ma
In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size. Such operation is commonly so-…
Removal of Curtaining Effects by a Variational Model with Directional Forward Differences
Jan Henrik Fitschen, Jianwei Ma, Sebastian Schuff
Focused ion beam (FIB) tomography provides high resolution volumetric images on a micro scale. However, due to the physical acquisition process the resulting images are often corru…