Preprints
https://doi.org/10.5194/se-2020-193
https://doi.org/10.5194/se-2020-193
02 Dec 2020
 | 02 Dec 2020
Status: this preprint has been withdrawn by the authors.

Seismological assessment of human activity levels during the COVID-19 pandemic

Jeongin Lee and Tae-Kyung Hong

Abstract. The COVID-19 virus has a high infection rate, spreading fast in the world. Lockdown and stay-at-home actions have been taken in many countries to reduce the rate of the virus spreading. The daytime ambient seismic noises in 11 major cities of 7 countries are assessed. Daytime seismic noises in 10 am to 6 pm at frequencies ≥ 2 Hz are assessed. The seismic noise levels are compared with the community mobility data that represent the human activities. The high-frequency seismic noise levels present high correlation with the human activities. The human activities decrease with the number of daily confirmed cases. The peak noise-level reductions in lockdown periods were as high as 42–96 %. The noise levels generally started to decrease since the days when the daily confirmed cases reached ~500. The noise level variation presents the lockdown progress. The noise level recovers with time since the end of lockdown. The high correlation between seismic noise level and community mobility suggests possible utilization of seismic noises for anonymous monitoring of human activities.

This preprint has been withdrawn.

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Jeongin Lee and Tae-Kyung Hong

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Jeongin Lee and Tae-Kyung Hong
Jeongin Lee and Tae-Kyung Hong

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This preprint has been withdrawn.

Short summary
The COVID-19 virus spreads fast throughout the world. There are no effective vaccines currently. Many countries locked down the cities to reduce the virus spreading. The efficiency and effect of social distancing on the virus spreading is important for this pandemic. However, there were no effective ways to measure the level of social distancing in real time. In this study, we infer the level of social distancing using the ambient seismic noise levels.