Narrow-Band Least-Squares Infrasound Array Processing

Journal Article · Fri Jun 10 00:00:00 UTC 2022 · Seismological Research Letters

DOI: https://doi.org/10.1785/0220220042 · OSTI ID:1879394

Iezzi, Alexandra M.  ^[1]; Matoza, Robin S.  ^[1]; Bishop, Jordan W.  ^[2]; Bhetanabhotla, Sneha ^[1]; Fee, David  ^[3]

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1. Univ. of California, Santa Barbara, CA (United States)
2. Univ. of Alaska, Fairbanks, AK (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
3. Univ. of Alaska, Fairbanks, AK (United States)

Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: Defense Threat Reduction Agency (DTRA); National Science Foundation (NSF); USDOE
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1879394
• Report Number(s): LA-UR-22-20994
• Journal Information: Seismological Research Letters, Journal Name: Seismological Research Letters Journal Issue: 5 Vol. 93; ISSN 0895-0695
• Publisher: Seismological Society of AmericaCopyright Statement
• Country of Publication: United States
• Language: English