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Detecting Seismic Events with Computer Vision: Applications for Fiber-Optic Sensing
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  • Solvi Thrastarson,
  • Robert Torfason,
  • Sara Klaasen,
  • Patrick Paitz,
  • Yesim CUBUK SABUNCU,
  • Kristín Jónsdóttir,
  • Andreas Fichtner
Solvi Thrastarson
ETH Zurich, Switzerland

Corresponding Author:[email protected]

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Robert Torfason
Genki Instruments
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Sara Klaasen
Swiss Federal Institute of Technology in Zurich
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Patrick Paitz
Swiss Federal Institute of Technology in Zurich
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Yesim CUBUK SABUNCU
Icelandic Meteorological Office
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Kristín Jónsdóttir
Icelandic Met Office, Iceland
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Andreas Fichtner
ETH Zurich
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Abstract

Seismologists working with fiber-optic sensing, commonly referred to as Distributed Acoustic Sensing (DAS), have yet to find an established way of automatically detecting signals of interest within its recordings. We propose a new research perspective within the field by examining the output of a DAS array as an image and processing the image to find signals of interest. In this manuscript, we show an example of such a method, where we automatically detect seismic events of interest within two different DAS datasets, finding, respectively 99 % and 96 % of the local earthquakes previously identified within the data by manual analysis. The method is based on simple image processing and computer vision techniques, which clean the image, and, ideally, leave nothing but the signal of interest. These simple image processing steps yield promising results, indicating that computer vision and image processing might have an immediate impact in geophysical applications of fiber-optic sensing.