SUMMARY
About a decade ago, noise-based monitoring became a key tool in seismology. One of the tools is passive image interferometry (PII), which uses noise correlation functions (NCF) to retrieve seismic velocity variations. Most studies apply PII to vertical components recording oceanic low-frequent ambient noise ( < 1 Hz). In this work, PII is applied to high-frequent urban ambient noise ( > 1 Hz) on three three-component sensors. With environmental sensors inside the subsurface and in the air, we are able to connect observed velocity variations with environmental parameters. Temperatures below 0 °C correlate well with strong shear wave velocity increases. The temperature sensors inside the ground suggest that a frozen layer of less than 5 cm thickness causes apparent velocity increases above 2 % , depending on the channel pair. The observations indicate that the different velocity variation retrieved from the different channel pairs are due to different surface wave responses inherent in the channel pairs. With dispersion curve modelling in a 1-D medium we can verify that surfaces waves of several tens of metres wavelength experience a velocity increase of several percent due to a centimetres thick frozen layer. Moreover, the model verifies that Love waves show larger velocity increases than Rayleigh waves. The findings of this study provide new insights for monitoring with PII. A few days with temperature below 0 °C can already mask other potential targets (e.g. faults or storage sites). Here, we suggest to use vertical components, which is less sensitive to the frozen layer at the surface. If the target is the seasonal freezing, like in permafrost studies, we suggest to use three-component sensors in order to retrieve the Love wave response. This opens the possibility to study other small-scale processes at the shallow subsurface with surface wave responses.
Temporal changes of surface wave velocity associated with major Sumatra earthquakes from ambient noise correlation
