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AI-based unmixing of medium and source signatures from seismograms: ground freezing patterns
  • Rene Steinmann,
  • Léonard Simon Seydoux,
  • Michel Campillo
Rene Steinmann
Université Grenoble Alpes

Corresponding Author:rene.steinmann@univ-grenoble-alpes.fr

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Léonard Simon Seydoux
Massachusetts Institute of Technology
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Michel Campillo
Université Grenoble Alpes
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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.