Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning
Abstract Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximate...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-10-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2024GL110672 |
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| _version_ | 1850229037424705536 |
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| author | Jiahui Kang Fabian Walter Patrick Paitz Johannes Aichele Pascal Edme Lorenz Meier Andreas Fichtner |
| author_facet | Jiahui Kang Fabian Walter Patrick Paitz Johannes Aichele Pascal Edme Lorenz Meier Andreas Fichtner |
| author_sort | Jiahui Kang |
| collection | DOAJ |
| description | Abstract Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards. |
| format | Article |
| id | doaj-art-bfb09a042b7b44cea68452cdd127aea6 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-bfb09a042b7b44cea68452cdd127aea62025-08-20T02:04:21ZengWileyGeophysical Research Letters0094-82761944-80072024-10-015119n/an/a10.1029/2024GL110672Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised LearningJiahui Kang0Fabian Walter1Patrick Paitz2Johannes Aichele3Pascal Edme4Lorenz Meier5Andreas Fichtner6Swiss Federal Institute for Forest Snow and Landscape Research Zürich SwitzerlandSwiss Federal Institute for Forest Snow and Landscape Research Zürich SwitzerlandSwiss Federal Institute for Forest Snow and Landscape Research Zürich SwitzerlandDepartment of Earth Sciences ETH Zürich Zürich SwitzerlandDepartment of Earth Sciences ETH Zürich Zürich SwitzerlandGeopraevent AG Zürich SwitzerlandDepartment of Earth Sciences ETH Zürich Zürich SwitzerlandAbstract Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.https://doi.org/10.1029/2024GL110672distributed acoustic sensingmachine learningprecursorsimage processingrepresentation learningearly warning |
| spellingShingle | Jiahui Kang Fabian Walter Patrick Paitz Johannes Aichele Pascal Edme Lorenz Meier Andreas Fichtner Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning Geophysical Research Letters distributed acoustic sensing machine learning precursors image processing representation learning early warning |
| title | Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning |
| title_full | Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning |
| title_fullStr | Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning |
| title_full_unstemmed | Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning |
| title_short | Automatic Monitoring of Rock‐Slope Failures Using Distributed Acoustic Sensing and Semi‐Supervised Learning |
| title_sort | automatic monitoring of rock slope failures using distributed acoustic sensing and semi supervised learning |
| topic | distributed acoustic sensing machine learning precursors image processing representation learning early warning |
| url | https://doi.org/10.1029/2024GL110672 |
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