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 …

We present a rank reduction algorithm that permits simultaneous reconstruction and random
noise attenuation of seismic records. We based our technique on multichannel singular …

Machine learning (ML) systems can automatically mine data sets for hidden features or
relationships. Recently, ML methods have become increasingly used within many scientific …

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 …

Seismic data recovery from data with missing traces on otherwise regular acquisition grids
forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery …

Regularization is a required component of geophysical-estimation problems that operate
with insufficient data. The goal of regularization is to impose additional constraints on the …

Seismic trace interpolation is an important technique because irregular or insufficient
sampling data along the spatial direction may lead to inevitable errors in multiple …

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 …

Although 3D seismic data are being acquired in larger volumes than ever before, the spatial
sampling of these volumes is not always adequate for certain seismic processes. This is …