Survey Designing for Blended Acquisition with Irregularly Sub-Sampled Geometries

Abstract

We introduce a workflow to derive survey parameters responsible for source blending as well as spatial sampling of detectors and sources. The proposed workflow iteratively performs the following three steps. The first step is
application of blending and sub-sampling to an unblended and well-sampled data. We then apply a closed-loop deblending and data reconstruction enabling a robust estimate of a deblended and reconstructed data. The residue
for a given design from this step is evaluated, and subsequently used by genetic algorithms (GAs) to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into a next iteration till they satisfy given stopping criteria. We also propose repeated encoding sequence (RES) used to form a parameter sequence in GAs, making the proposed designing workflow computationally affordable. We demonstrate the results of the workflow using numerically simulated examples that represent blended dispersed source array data. Difference attributable only to a way to design parameters is easily recognizable. The optimized parameters yield clear improvement of deblending and data reconstruction quality and subsequently provide optimal acquisition scenarios. Additionally, comparison among different optimization schemes illustrates ability of GAs along with RES to efficiently find better solutions.