Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach

TL;DR

This paper introduces a primal-dual alternating method for 3D seismic data interpolation that efficiently handles large-scale data volumes, outperforming traditional level-set methods in accuracy and computational efficiency.

Contribution

A novel primal-dual alternating algorithm for residual constrained interpolation of large seismic data volumes, leveraging matrix factorization and block-coordinate updates.

Findings

Successfully interpolated a 5D seismic data volume with 80% missing data.

Competitive performance against state-of-the-art level-set algorithms.

Demonstrated effectiveness on complex synthetic seismic data.

Abstract

Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for these techniques. We propose a new approach to solve residual constrained formulations for interpolation. We represent the data volume using matrix factors, and build a block-coordinate algorithm with constrained convex subproblems that are solved with a primal-dual splitting scheme. The new approach is competitive with state of the art level-set algorithms that interchange the role of objectives with constraints. We use the new algorithm to successfully interpolate a large scale 5D seismic data volume, generated from the geologically complex synthetic 3D Compass velocity model, where 80% of the data has been removed.