Beating level-set methods for 3D seismic data interpolation: a primal-dual alternating approach
This work addresses the high acquisition costs in seismic workflows by improving data interpolation methods, though it appears incremental as it builds on existing low-rank formulations and competes with level-set algorithms.
The authors tackled the problem of interpolating large-scale 3D seismic data from subsampled acquisitions by proposing a primal-dual alternating approach, achieving successful interpolation on a complex synthetic dataset with 80% of the data removed.
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.