Zooming out: Seasonal changes shown by the background seismic wavefield in the Swiss Alps and Molasse basin

Ambient seismic noise is a highly useful signal to monitor various Earth structures and processes over time. Through its excitation at the Earth’s surface by the oceans, wind and other sources, it also provides an observational basis to study the interaction between the solid Earth and its oceans and atmosphere.

While ambient noise has been used extensively for monitoring crust and soil with coda wave passive image interferometry, it remains challenging to localize and quantitatively model the observed changes. Ballistic waves retrieved by ambient noise cross-correlation, which would provide a more straightforward means to interpret observed changes, are only considered an acceptable observable for monitoring under specific circumstances due to the high spatio-temporal variability of ambient noise sources which may bias the measurements.

With the motivation to understand such biases better, we investigate the time-dependent behaviour of attenuation and phase velocity on a regional-scale, 20-year cross-correlation dataset from Switzerland, including stations in the Jura, the Molasse basin and the Alps. Seasonal variations in the composition of the ambient seismic noise field due to the generation of microseismic noise by the ocean have been previously observed. Here, we observe seasonal phase velocity and surface wave attenuation changes, which we further compare to conventional dv/v measurements and time-dependent ambient noise coda-Q measurements. To investigate these changes more quantitatively, we model ambient noise correlations numerically using pre-computed Green’s function libraries for a 3-D Earth model from SPECFEM3D_globe and oceanographically constrained secondary microseism source proxy maps. With these models we aim to determine whether the observed seasonal variations can be explained by ocean microseism source effects.

With this work, we intend to contribute to the quantitative understanding and usage of ambient noise correlations, in particular for the secondary microseism, and ultimately to detailed and interpretable time-dependent monitoring of the crust.