Revealing the signature of ground frost in continuous seismic data with
machine learning
Abstract
We study how ground frost affects the ambient seismic wavefield recorded
by a three-component broadband sensor. By applying machine learning
algorithms on continuous seismic data, we can retrieve the seismic
signature of the continuous freeze and thaw process at the surface of
the ground. The retrieved signature reveals that the presence of ground
frost imprints the amplitude of the ambient seismic wavefield, and the
energy ratio between horizontal and vertical components (H/V). A
regression model can even predict diurnal freeze and thaw patterns based
on the seismic data. Thus, we assume that slight changes in the physical
properties of the frozen surface, such as the thickness, alter the
seismic wavefield. Models of the subsurface with different properties of
the ground frost agree with the observations from the field. The
penetration depth of the ground frost, the temperature of the frozen
ground, and the presence of different modes in the wavefield determine
how the seismic wavefield is changing. The findings of this study show
the potential of a single seismic station for monitoring frozen bodies
near the surface, such as permafrosts.