Abstract
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.
Highlights
For a long time in the history of seismology ambient seismic noise disturbed continuous recordings and seismologists developed methods to eliminate it
Three three-component seismometers were installed in Hamburg (Germany) and velocity variations in the near subsurface were monitored with the Passive Image Interferometry (PII) method
A stack of 576 Noise Correlation Functions (NCF), equalling four days of data, produced stable NCFs, which were used for the stretching method. dv/v time-series in the frequency band 1 − 6 Hz were created with all components
Summary
For a long time in the history of seismology ambient seismic noise disturbed continuous recordings and seismologists developed methods to eliminate it. Environmental seismology uses seismic wave fields and natural seismic sources to study the coupling between the earth and the external environment (Larose et al, 2015). In the last years many studies used ambient seismic noise to monitor different environmental effects on seismic velocities. Lecocq et al (2017) used 30 years of continuous ambient seismic noise recordings of the Gräfenberg array to track the aquifer These studies showed that noise-based monitoring is able to detect minor changes in the near subsurface and that it is a new method to study the coupling between the earth’s near subsurface and the external environment. Observed seasonal variations in the seismic velocity can have several causes, e.g. temperature, water content or changes of the noise sources The last section covers dispersion curve modelling as a possible explanation for the observations
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