Seismic channel interpretation involves detecting channel structures, which often appear as
meandering shapes in 3D seismic images. Many conventional methods are proposed for …

Seismic high-resolution reconstruction is a key step in retrieving high-resolution components
to characterize subsurface geological structures. Recently, deep learning (DL) has emerged …

Abstract Time-lapse (or 4-D) seismic data play an important role in monitoring the spatial CO
2 distribution during and after the injection period. However, traditional interpretation or …

The resolution of seismic data determines the ability to characterize individual geological
structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for …

Seismic high-resolution (HR) reconstruction is a crucial process for identifying increasingly
thin layers from observed seismic data. Nowadays, machine learning (ML) has been …

Applying deep-learning models to geophysical applications has attracted special attentions
during the past a couple of years. There are several papers published in this domain …

Deep learning has shown promising performance in interpreting and processing of seismic
data. Currently, many trace-by-trace deep learning methods have been successfully applied …