A Scalable Algorithm for Cross-correlations of Compressed Ambient Seismic Noise

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.


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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.




Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics


interferometry, Ambient seismic noise, algorithms, big data, cross-correlations, near-surface imaging, seismic imaging


Published: 2019-04-17 09:10

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GNU Lesser General Public License (LGPL) 2.1

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