AI-based unmixing of medium and source signatures from seismograms:
ground freezing patterns
Abstract
Seismograms always result from mixing many sources and medium changes
that are complex to disentangle, witnessing many physical phenomena
within the Earth. With artificial intelligence (AI), we isolate the
signature of surface freezing and thawing in continuous seismograms
recorded in a noisy urban environment. We perform a hierarchical
clustering of the seismograms and identify a pattern that correlates
with ground frost periods. We further investigate the fingerprint of
this pattern and use it to track the continuous medium change with high
accuracy and resolution in time. Our method isolates the effect of the
ground frost and describes how it affects the horizontal wavefield. Our
findings show how AI-based strategies can help to identify and
understand hidden patterns within seismic data caused either by medium
or source changes.