Seismic reflection utilizes sound waves transmitted into the subsurface, reflected at rock
boundaries, and recorded at the surface. Interpretation of their travel times and amplitudes …

Extracting horizons and detecting faults in a seismic image are basic steps for structural
interpretation and important for many seismic processing schemes. A common ground of the …

Seismic horizon picking via deep learning models have been advanced rapidly and proven
popular. However, the prediction result is highly depended on the quality of the train set and …

A convolutional neural network (CNN) is a powerful tool used for seismic interpretation. It
does not require manual intervention and can automatically detect geological structures …

Accurately estimating seismic volumetric dip is a crucial task for subsequent seismic
processing and interpretation. The traditional window based dip estimation methods, such …

Seismic interpretation aims to extract quantitative and interpretable attributes from a seismic
image produced using some migration method to inform characteristics of a subsurface …

Simultaneous source acquisition technology can greatly improve seismic acquisition
efficiency. However, due to continuous shooting and serious crosstalk noise of the adjacent …

Horizon picking is of paramount importance in seismic interpretation because it has a
significant impact on subsequent interpretation and inversion. Although manual and various …

By increasing the source density, the blended data can significantly improve the seismic
data quality. However, the blended data cannot be directly used in the subsequent seismic …

One of the major hot topics in seismic data processing is the reconstruction of successively
sampled seismic data. There are numerous traditional methods proposed for addressing this …