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Detection of Infrasound Signals and Sources Using a Dense Seismic Network

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Infrasound Monitoring for Atmospheric Studies

Abstract

This new era of massive datasets gives us the opportunity to examine Earth structure and geophysical phenomena in more detail than previously possible. Large datasets hold much promise for transformative research but require new analytical methods that are both efficient and capable of extracting useful information from faint signals immersed in noise. With these needs in mind, we developed the AELUMA (Automated Event Location Using a Mesh of Arrays) method that recasts any dense network of sensors as a distributed mesh of small triangular arrays (triads). Each array provides a local estimate of signal properties. Information from arrays distributed across the footprint of the network is combined to estimate the source origin time and location. The process is repeated without oversight to catalog events that have occurred over a period of time. We have analyzed ground-coupled airwaves recorded on vertical component broadband seismometers of the USArray Transportable Array (TA). We estimate the accuracy of the AELUMA algorithm using ground truth events at the Utah Test and Training Range (UTTR). In a study of 23 surface explosions, the mean AELUMA source location estimate is 8.6 km northwest of the ground truth location. The origin time estimates were late for most events. The mean time misfit is 19 s with a standard deviation of 39 s. We attribute the positive bias in origin time estimates to signal dispersion, as the AELUMA method estimates the time of the signal’s peak amplitude, not its onset. A comparison of AELUMA and a reverse time migration method indicates that AELUMA is more sensitive to faint signals from weak events and the event locations are more accurate in space and time. A catalog of acoustic activity from across the continental United States in the band from 0.7 to 4.0 Hz includes 7935 events that were detected by 10 or more triads. Most events were clustered into hotspots and are likely anthropogenic.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. EAR-1358520. We thank Relu Burlacu for providing ground truth information on the explosions at UTTR. This study would not have been possible without the high-quality data from the USArray TA. The TA is part of the Incorporated Research Institutions for Seismology (IRIS) EarthScope program. The authors thank Jelle Assink for a constructive review.

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Authors and Affiliations

  1. Laboratory for Atmospheric Acoustics, Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, 92093-0225, USA

    Catherine de Groot-Hedlin & Michael Hedlin

Authors
  1. Catherine de Groot-Hedlin
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  2. Michael Hedlin
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Corresponding authors

Correspondence to Catherine de Groot-Hedlin or Michael Hedlin .

Editor information

Editors and Affiliations

  1. CEA, DAM, DIF, Arpajon, France

    Alexis Le Pichon

  2. CEA, DAM, DIF, Arpajon, France

    Elisabeth Blanc

  3. SHTI, LATMOS/IPSL, Guyancourt, France

    Alain Hauchecorne

Appendices

Supporting Information

National event catalogs in three bands (0.5–2.0 Hz, 0.7–4.0 Hz, and 2–8 Hz) computed using seismic data collected from 2006 through 2014. Catalog of events in the eastern United States computed using infrasonic data collected in 2012, 2013, and 2014. The AELUMA code is now available on request through product@iris.washington.edu.

Appendix

The AELUMA method is applicable to a wide range of network configurations and signal types, given that parameters are set correctly for signal type sought and the array configuration. Table 21.2 describes the main parameters needed; values used for this study are listed in the final column. The AELUMA code is available on request through product@iris.washington.edu.

Table 21.2 AELUMA parameters
Full size table

The phase and celerity values depend on the signal type sought. For example, for infrasound, the true phase velocities range from about 320 m/s and up, depending on whether the signal propagates horizontally across the triad or has steep incidence from above. The phase velocities are allowed a wide range, to allow for errors in phase velocity and propagation angle across the triad that derives from the imperfect fit to Eq. 1.

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de Groot-Hedlin, C., Hedlin, M. (2019). Detection of Infrasound Signals and Sources Using a Dense Seismic Network. In: Le Pichon, A., Blanc, E., Hauchecorne, A. (eds) Infrasound Monitoring for Atmospheric Studies. Springer, Cham. https://doi.org/10.1007/978-3-319-75140-5_21

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