Seismic data interpolation is a longstanding issue. Most current methods are only suitable
for randomly missing cases. To deal with regularly missing cases, an antialiasing strategy …

Seismic data reconstruction is one of the essential steps in the seismic data processing.
Recently, the deep learning (DL) models have attracted huge attention in seismic …

The Cadzow rank-reduction method can be effectively utilized in simultaneously denoising
and reconstructing 5-D seismic data that depend on four spatial dimensions. The classic …

We propose an algorithm for seismic trace interpolation using generative adversarial
networks, a type of deep neural network. The method extracts feature vectors from the …

We propose to denoise and reconstruct the 3D seismic data simultaneously using an
unsupervised deep learning (DL) framework, which does not require any prior information …

Having dense and regularly sampled data is becoming increasingly important in seismic
processing. However, due to physical or financial constraints, seismic data sets can be often …

Seismic data processing algorithms greatly benefit from regularly sampled and reliable data.
Therefore, interpolation and denoising play a fundamental role as one of the starting steps of …

Seismic data reconstruction is always an essential step in the field of seismic data
processing. Effective reconstruction methods can obtain high-density information at low-cost …

ABSTRACT A common issue of seismic data analysis consists in the lack of regular and
densely sampled seismic traces. This problem is commonly tackled by rank optimization or …

Seismic data interpolation is an effective way of recovering missing traces and obtaining
enough information for subsequent processing. Unlike traditional methods, deep neural …