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

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.

Deep preconditioners and their application to seismic wavefield processing

“…Whilst this approach circumvents the need for any training data, it is currently hindered by very slow convergence and it is shown to be incapable of recovering strongly aliased events. Anti-aliasing, slope-based regularization (Picetti et al, 2021) or a POCSinspired regularization (Park et al, 2020) have been further proposed to increase the interpolation capabilities of such deep prior networks.…”