PSIN: A scalable, Parallel algorithm for Seismic INterferometry of large-N ambient-noise data. (August 2016)

• Record Type: Journal Article
• Authors: Chen, Po; Taylor, Nicholas J.; Dueker, Ken G.; Keifer, Ian S.; Wilson, Andra K.; McGuffy, Casey L.; Novitsky, Christopher G.; Spears, Alec J.; Holbrook, W. Steven
• Is Part Of: Computers & geosciences. Volume 93(2016)
• Page Start: 88
• Page End: 95
• Publication Date: 2016-08
• Subjects: Seismic interferometry -- Ambient-noise -- Parallel algorithm -- Message-passing interface; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2016.05.003
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Seismic interferometry is a technique for extracting deterministic signals (i.e., ambient-noise Green's functions) from recordings of ambient-noise wavefields through cross-correlation and other related signal processing techniques. The extracted ambient-noise Green's functions can be used in ambient-noise tomography for constructing seismic structure models of the Earth's interior. The amount of calculations involved in the seismic interferometry procedure can be significant, especially for ambient-noise datasets collected by large seismic sensor arrays (i.e., "large-N" data). We present an efficient parallel algorithm, named pSIN ( P arallel S eismic IN terferometry), for solving seismic interferometry problems on conventional distributed-memory computer clusters. The design of the algorithm is based on a two-dimensional partition of the ambient-noise data recorded by a seismic sensor array. We pay special attention to the balance of the computational load, inter-process communication overhead and memory usage across all MPI processes and we minimize the total number of I/O operations. We have tested the algorithm using a real ambient-noise dataset and obtained a significant amount of savings in processing time. Scaling tests have shown excellent strong scalability from 80 cores to over 2000 cores. Highlights: A new parallel algorithm for seismic interferometry pSIN is developed. pSIN requires reading noise data only once and disk I/O overhead is minimized. Experiments using a real dataset shows close to ideal scalability of pSIN .