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Updated: Aug 25, 2025

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
Published on: August 7, 2017
AI-Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns
René Steinmann1, Léonard Seydoux1,2, Michel Campillo1
1ISTerre Équipe Ondes et Structures Université Grenoble-Alpes UMR CNRS 5375 Gières France.
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.
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