Reconstruction of Compressively-Sampled Land Data

Abstract

Acquiring economical land data with compressive sensing requires data reconstruction. In the presence of complex near-surface weathering layers, which on their own typically pose a challenge to processing densely sampled data, data reconstruction suffers. The conventional approach of near-surface correction followed by interpolation rely on knowledge of the subsurface. However, obtaining a velocity model is difficult from subsampled data influenced by the weathering layers. To avoid that, we propose to reconstruct the data with a model-independent rank-reduction-based near-surface correction followed by interpolation. We showcase the proposed reconstruction on synthetic data. A field data example will also be presented during the meeting to demonstrate the potential of the method.