Abstract. Studying the uppermost structure of the subsurface is a
necessary part of solving many practical problems (exploration of minerals,
groundwater studies, geoengineering, etc.). The practical application of active
seismic methods for these purposes is not always possible for different reasons, such as logistical difficulties, high cost of work, and a high
level of seismic and acoustic noise. That is why developing and improving
passive seismic methods is one of the important problems in applied
geophysics. In our study, we describe a way of improving the quality of
empirical Green's functions (EGFs), evaluated from high-frequency ambient
seismic noise, by using the advanced technique of cross-correlation function stacking in the time domain (in this paper we use term “high-frequency”
for frequencies higher than 1 Hz). The technique is based on the global
optimization algorithm, in which the optimized objective function is a
signal-to-noise ratio of an EGF, retrieved at each iteration. In comparison
to existing techniques, based, for example, on weight stacking of
cross-correlation functions, our technique makes it possible to significantly increase the signal-to-noise ratio and, therefore, the quality of the
EGFs. The technique has been tested with the field data acquired in an area
with a high level of industrial noise (Pyhäsalmi Mine, Finland) and in an
area with a low level of anthropogenic noise (Kuusamo Greenstone Belt,
Finland). The results show that the proposed technique can be used for
the extraction of EGFs from high-frequency seismic noise in practical problems
of mapping of the shallow subsurface, both in areas with high and low levels
of high-frequency seismic noise.
Estimation and Correction of Sampling Time Offsets in Tiadcs using Optimization Algorithm
