Analyzing Ambient Seismic Noise Collected by Distributed Acoustic Sensing (DAS)

Eileen Martin, Stanford University

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Distributed acoustic sensing (DAS) is a technology that repurposes a standard fiber optic cable as a low-cost seismic monitoring device. We are particularly interested in developing a network to continuously monitor permafrost melt. DAS can easily collect huge amounts of information over a long distance, but those data are challenging to utilize due to a low signal-to-noise ratio and insensitivity to waves at certain frequencies and incoming angles. We installed a trenched DAS array to monitor the speed of waves in the near subsurface using only low-amplitude ambient seismic noise generated primarily by traffic and trains. We show that interferometric methods applied to a very small subset of the data yield coherent estimates of the response to virtual seismic sources at each segment of the cable. The estimates from the DAS data for several types of cables show reasonable Rayleigh wave velocities compared to the results of traditional sensors.

In addition to our proof of concept on real data, we also propose a simple new algorithm for calculating dispersion images from ambient seismic noise that yields better numerical accuracy than existing algorithms, scales linearly with the number of sensors (even for irregular three-dimensional surveys), and is easily parallelizable. This lays the groundwork for larger-scale permafrost monitoring experiments that not only can collect large volumes of data at low cost but also easily analyze the data as they stream in.

Abstract Author(s): Eileen Martin