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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.
Artificial intelligence (AI) helps detect ground frost effects on seismic data. This method tracks continuous medium changes using seismograms, revealing hidden patterns in noisy urban environments.
Area of Science:
- Geophysics
- Artificial Intelligence
- Environmental Science
Background:
- Seismograms are complex, reflecting multiple sources and Earth medium changes.
- Disentangling these signals, especially in noisy urban settings, is challenging.
- Understanding environmental impacts on seismic wave propagation is crucial.
Purpose of the Study:
- To isolate and identify the seismic signature of surface freezing and thawing.
- To develop a method for tracking continuous medium changes using seismic data.
- To demonstrate the utility of AI in analyzing complex geophysical signals.
Main Methods:
- Hierarchical clustering of continuous seismograms.
- Pattern recognition correlating seismic data with ground frost periods.
- Analysis of the horizontal wavefield affected by frost.
Main Results:
- A distinct seismic pattern associated with ground frost periods was identified.
- The method accurately tracks continuous medium changes with high temporal resolution.
- The influence of ground frost on the horizontal seismic wavefield was characterized.
Conclusions:
- AI-based strategies are effective for identifying hidden patterns in seismic data.
- Surface freezing and thawing leave a detectable signature in seismograms.
- This approach enhances our understanding of environmental influences on geophysical phenomena.
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