Tracking anthropogenic seismic noise variations during the COVID-19 pandemic using fiber optic sensors

Abstract:

Recent studies have reported world-wide quieting of seismic noise recorded by seismometers due to COVID-19 lockdown measures to reduce the spread of coronavirus. Detailed study of anthropogenic seismic noise variations in a city scale could provide a means to investigate the COVID-19 impact on human activities, evaluate the effectiveness of these measures, and finally provide feedback to adjust the measure in urban areas. While sparse distribution of seismometers in a city-scale area to characterize noise lack of spatiotemporal resolution, considering complex anthropogenic noise sources in spatiotemporal domains, distributed acoustic sensing (DAS) could provide high spatiotemporal resolution seismic data in cities, by converting existing optic fibers to dense sensor arrays with low-cost and low-maintenance.

Here we report the COVID-19 anthropogenic noise monitoring results from the 4-km-long DAS array in the city of State College, PA. We observe a wild drop of seismic noise level across the array after the stay-at-home (lockdown) order was issued on March 18^th 2020 in PA. And the seismic noise gradually increased back after lifting the restrictions on business and individual mobility on May 1^st 2020. Temporally, anthropogenic noises including footstep, traffic and industrial construction in the 1-50 Hz are reduced and then increased significantly up to +/-10 dB. Spatially, DAS can capture the noise variation in the city block scale. Noises in “rural” area, main campus and teaching areas are dominated by environmental noise, traffic noise and human-generated noise, respectively. During lockdown, noises in rural areas remain relatively stable but high-frequency traffic noise (>10 Hz) on main campus and low-frequency noise (<10 Hz) in teaching areas reduced significantly. Our results that correlate well with independent mobility data suggest that DAS can serve as an innovative approach to monitor the dynamics of COVID-19 lockdown measures with high spatiotemporal resolution in future cities.