Abstract
Abstract. In mountainous, cold temperate and polar sites, the presence of snow cover can affect relative seismic velocity changes (dV/V) derived from ambient noise correlation, but this relation is relatively poorly documented and ambiguous. In this study, we analyzed raw seismic recordings from a snowy flat field site located above Davos (Switzerland), during one entire winter season (from December 2018 to June 2019). We identified three snowfall events with a substantial response of dV/V measurements (drops of several percent between 15 and 25 Hz), suggesting a detectable change in elastic properties of the medium due to the additional fresh snow. To better interpret the measurements, we used a physical model to compute frequency-dependent changes in the Rayleigh wave velocity computed before and after the events. Elastic parameters of the ground subsurface were obtained from a seismic refraction survey, whereas snow cover properties were obtained from the snow cover model SNOWPACK. The decrease in dV/V due to a snowfall was well reproduced, with the same order of magnitude as observed values, confirming the importance of the effect of fresh and dry snow on seismic measurements. We also observed a decrease in dV/V with snowmelt periods, but we were not able to reproduce those changes with our model. Overall, our results highlight the effect of the snow cover on seismic measurements, but more work is needed to accurately model this response, in particular for the presence of liquid water in the snowpack.
Highlights
The method of seismic ambient noise correlation is broadly used to monitor the subsurface, in order to detect physical processes in the surveyed medium such as changes in rigidity, fluid injection or cracking (Sens-Schönfelder and Wegler, 2006; Larose et al, 2015)
As there were no meteorological measurements as input data at the site, we interpolated measurements from seven automatic weather stations (AWSs) within a radius of 20 km of the
Model results are shown with error bars corresponding to snow elastic parameter uncertainties (P- and S-wave velocities ±10 %) in order to assess the sensitivity of the model to snow modeling
Summary
The method of seismic ambient noise correlation is broadly used to monitor the subsurface, in order to detect physical processes in the surveyed medium such as changes in rigidity, fluid injection or cracking (Sens-Schönfelder and Wegler, 2006; Larose et al, 2015) Several observables such as relative velocity changes of surface waves or changes in waveforms can be continuously measured. These latter environmental effects on dV /V have been studied both experimentally and numeri-
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