AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns
Name
Geophysical Research Letters - 2022 - Steinmann - AI‐Based Unmixing of Medium and Source Signatures From Seismograms .pdf
Description
Published version
Size
3.1 MB
Format
Adobe PDF
Checksum (MD5)
f8d9e559aa404879d89e5c91ed2691c7
Author(s)
Steinmann, René
Seydoux, Léonard
Campillo, Michel
Date Issued
August 3, 2022
Journal
Geophysical Research Letters
Publisher
American Geophysical Union
Citation
Steinmann, R., Seydoux, L., & Campillo, M. (2022). AI-based unmixing of medium and source signatures from seismograms: Ground freezing patterns. Geophysical Research Letters, 49, e2022GL098854.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2022gl098854
