Anti-Aliasing Add-On for Deep Prior Seismic Data Interpolation

TL;DR

This paper enhances Deep Prior seismic data interpolation by adding a directional Laplacian regularizer, reducing aliasing effects and improving reconstruction accuracy in highly decimated and noisy datasets.

Contribution

It introduces a novel regularization technique for Deep Prior inversion that leverages slope information to mitigate aliasing in seismic data interpolation.

Findings

Reduced aliasing in interpolated seismic data

Improved reconstruction accuracy with noisy and corrupted data

Demonstrated effectiveness through numerical examples

Abstract

Data interpolation is a fundamental step in any seismic processing workflow. Among machine learning techniques recently proposed to solve data interpolation as an inverse problem, Deep Prior paradigm aims at employing a convolutional neural network to capture priors on the data in order to regularize the inversion. However, this technique lacks of reconstruction precision when interpolating highly decimated data due to the presence of aliasing. In this work, we propose to improve Deep Prior inversion by adding a directional Laplacian as regularization term to the problem. This regularizer drives the optimization towards solutions that honor the slopes estimated from the interpolated data low frequencies. We provide some numerical examples to showcase the methodology devised in this manuscript, showing that our results are less prone to aliasing also in presence of noisy and corrupted data.