4.6 Article

Pushing the limit of earthquake detection with distributed acoustic sensing and template matching: a case study at the Brady geothermal field

Journal

GEOPHYSICAL JOURNAL INTERNATIONAL
Volume 215, Issue 3, Pages 1583-1593

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/gji/ggy359

Keywords

Earthquake source observations; Induced seismicity; Seismic instruments

Funding

  1. Nation Science Foundation [1722879]
  2. Caltech Discovery Fund
  3. Geothermal Technologies Office of the U.S. Department of Energy [DE-EE0006760]
  4. Division Of Earth Sciences
  5. Directorate For Geosciences [1722879] Funding Source: National Science Foundation

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Template matching has been widely applied in the detection of earthquakes and other seismic events due to its power in detecting weak signals. Recent studies using synthetics have shown that application of template matching to large-N arrays can potentially detect earthquakes substantially below the noise level. Here we apply template matching to the distributed acoustic sensing (DAS) data recorded in the Brady Hot Springs geothermal field, Nevada. Using 5 catalogued events, we detect 116 events and find 68 of them well below the noise level. We confirm 112 events are true earthquakes by examining the patterns of their sensor-to-sensor cross-correlation sections. This demonstrates that the combination of DAS and template matching has capability to detect microseismicity below the noise level, which is unusual for conventional seismic arrays and methods. With the updated catalogue, we observe a surge of earthquakes during the shutdown of a geothermal power plant nearby. In addition, the rapid increases in the downhole pressure record coincide with intense swarms of earthquakes. These observations show a strong correlation between the seismicity frequencies and the downhole pressure changes. Finally, we investigate several factors that may affect the detection performance and compare different strategies for spatial down-sampling, in order to provide helpful insights for future large-N design and data processing.

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