Lithofacies classification is an indispensable procedure in well logging and seismic data
interpretation. We propose a novel deep classified autoencoder learning approach to …

The field acquired seismic data are often irregular, which affects the accuracy of subsequent
processing algorithms. We develop a framework based on a dual-attention-based wavelet …

Simultaneous seismic data reconstruction and denoising is a hot research topic. The sparse
representation method based on dictionary learning is one of the most effective methods to …

Seismic data interpolation is a vital technology for improving seismic data density. In recent
years, deep learning approaches have demonstrated significant potential in this field …

Seismic interpolation is a widely adopted method to improve the resolution of seismic
images. During the interpolation of regularly downsampled seismic data, the aliasing …

With spring up of infrared imaging related industry, infrared imaging technology has become
mainstream development direction of intelligent photoelectrical detection due to its good …

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 …

Missing traces are a common problem in seismic data acquisition, which can affect the
quality of subsequent processing and interpretation. Therefore, seismic data interpolation is …

Low‐rank reconstruction methods assume that noiseless and complete seismic data can be
represented as low‐rank matrices or tensors. Therefore, denoising and recovery of missing …

The undersampled seismic data may suffer from the degraded quality and pose negative
impacts on subsequent processing procedures. Seismic data interpolation is a cost-saving …