Detecting Seismic Events with Computer Vision: Applications for
Fiber-Optic Sensing
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.