Eileen Martin

Companies and academic geophysicists are increasingly collecting continuous seismic data on denser arrays, and are looking to a variety of lossy compression methods to store and quickly access this data. Some researchers turn to ambient noise interferometry for low-cost near-surface imaging to avoid to cost and permitting required for active source experiments, but the computation can be very expensive. For each window of time, typical ambient noise interferometry scales as the product of the number of time samples per window and the number of sensors squared. This paper proposes a new algorithm for data stored in a low-rank matrix factorized form, performing interferometry in compressed form, and separating scalability in sensors from time samples. Application to real data shows nearly identical results at orders of magnitude lower cost. The algorithm can be extended to tensor compressions, averaging cross-correlations over many time windows.