Traditional supervised denoising networks learn network weights through “black box”(pixel-
oriented) training, which requires clean training labels. The inability of such denoising …

Reconstruction of missing traces from seismic data is traditionally handled by physical
methods with good interpretability. Popular deep learning methods provide promising end-to …

Low-rank approximation has emerged as a promising technique for recovering five-
dimensional (5D) seismic data, yet the quest for higher accuracy and stronger rank …

Seismic denoising is an essential step for seismic data processing. Conventionally,
dictionary learning (DL) methods for seismic denoising always assume the representation …

The dictionary learning method has been successfully applied to denoise and interpolate
seismic data. However, this method cannot be used to adequately interpret weak seismic …

Seismic data interpolation plays a crucial role in obtaining dense and regularly sampled
data, contributing to improving the quality of seismic data in seismic exploration. Sparsity …

Recently, multi-trace impedance inversion has attracted great interest in seismic exploration
because it improves the horizontal continuity and fidelity of the inversion results by exploiting …

Seismic data reconstruction is an essential and yet fundamental step in seismic data
processing workflow, which is of profound significance to improve migration imaging quality …

The problem of recovering the complete seismic data from undersampled field-observed
data is a long-term challenge. Many recent efforts to address this problem develop model …

The nine components (9C) seismic data acquired with three-component (3C) sources and
3C receivers is beneficial to the inversion of lithologic reservoirs with high resolution …