Distributed Acoustic Sensing in Volcano-Glacial Environments-Mount Meager, British Columbia

We demonstrate the logistic feasibility and scientific potential of distributed acoustic sensing (DAS) in alpine volcano-glacial environments that are subject to a broad range of natural hazards. Our work considers the Mount Meager massif, an active volcanic complex in British Columbia, estimated to have the largest geothermal potential in Canada, and home of Canada's largest recorded landslide in 2010. From September to October 2019, we acquired continuous strain data, using a 3-km long fiber-optic cable, deployed on a ridge of Mount Meager and on the uppermost part of a glacier above 2,000 m altitude. The data analysis detected a broad range of unexpectedly intense, low-magnitude, local seismicity. The most prominent events include long-lasting, intermediate-frequency (0.01-1 Hz) tremor, and high-frequency (5-45 Hz) earthquakes that form distinct spatial clusters and often repeat with nearly identical waveforms. We conservatively estimate that the number of detectable high-frequency events varied between several tens and nearly 400 per day. We also develop a beamforming algorithm that uses the signal-to-noise ratio (SNR) of individual channels, and implicitly takes the direction-dependent sensitivity of DAS into account. Both the tremor and the high-frequency earthquakes are most likely related to fluid movement within Mount Meager's geothermal reservoir. Our work illustrates that DAS carries the potential to reveal previously undiscovered seismicity in challenging environments, where comparably dense arrays of conventional seismometers are difficult to install. We hope that the logistics and deployment details provided here may serve as a starting point for future DAS experiments.

Automated summary

This study demonstrates the logistic feasibility and scientific potential of using distributed acoustic sensing (DAS) in challenging environments like the Mount Meager alpine volcano-glacial region, detecting unexpected seismic activity and developing a beamforming algorithm based on signal-to-noise ratios.