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 …

Geophysical data interpolation has attracted much attention in the past decades. While a
variety of methods are well established for either regularly sampled or irregularly sampled …

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 …

ABSTRACT A key step in sparsifying signals is the choice of a sparsity-promoting dictionary.
There are two basic approaches to design such a dictionary: the analytic approach and the …

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 …

As a major concern, the existence of unwanted energy and missing traces in seismic data
acquisition can degrade interpretation of such data after processing. Instead of analytical …

The seislet transform uses a prediction operator that is connected to the local slope or
frequency of seismic events. We have combined the 1D nonstationary seislet transform with …

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 …

We have developed an interpolation method based on the denoising convolutional neural
network (CNN) for seismic data. It provides a simple and efficient way to break through the …

Simultaneous seismic data denoising and reconstruction is a currently popular research
subject in modern reflection seismology. Traditional rank-reduction based 3D seismic data …