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NoisePy; a new high-performance Python tool for ambient-noise seismology

Chengxin Jiang and Marine A. Denolle
NoisePy; a new high-performance Python tool for ambient-noise seismology
Seismological Research Letters (April 2020) 91 (3): 1853-1866

Abstract

The fast-growing interests in high spatial resolution of seismic imaging and high temporal resolution of seismic monitoring pose great challenges for fast, efficient, and stable data processing in ambient-noise seismology. This coincides with the explosion of available seismic data in the last few years. However, the current computational landscape of ambient seismic field seismology remains highly heterogeneous, with individual researchers building their own homegrown codes. Here, we present NoisePy-a new high-performance python tool designed specifically for large-scale ambient-noise seismology. NoisePy provides most of the processing techniques for the ambient field data and the correlations found in the literature, along with parallel download routines, dispersion analysis, and monitoring functions. NoisePy takes advantage of adaptable seismic data format, a parallel input and output enabled HDF5 data format designed for seismology, for a structured organization of the cross-correlation data. The parallel computing of NoisePy is performed using Message Passing Interface and shows a strong scaling with the number of cores, which is well suited for embarrassingly parallel problems. NoisePy also uses a small memory overhead and stable memory usage. Benchmark comparisons with the latest version of MSNoise demonstrate about four-time improvement in compute time of the cross correlations, which is the slowest step of ambient-noise seismology. NoisePy is suitable for ambient-noise seismology of various data sizes, and it has been tested successfully at handling data of size ranging from a few GBs to several tens of TBs.


ISSN: 0895-0695
EISSN: 1938-2057
Serial Title: Seismological Research Letters
Serial Volume: 91
Serial Issue: 3
Title: NoisePy; a new high-performance Python tool for ambient-noise seismology
Affiliation: Harvard University, Department of Earth and Planetary Sciences, Cambridge, MA, United States
Pages: 1853-1866
Published: 20200401
Text Language: English
Publisher: Seismological Society of America, El Cerrito, CA, United States
References: 106
Accession Number: 2020-039415
Categories: Seismology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 2 tables
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2022, American Geosciences Institute. Abstract, Copyright, Seismological Society of America. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 202025
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