Generalizable deep learning models for predicting laboratory earthquakes
Abstract Machine learning models can predict laboratory earthquakes using Acoustic emission, the lab equivalent of microseismicity, and changes in fault zone elastic properties during the lab seismic cycle. Applying them to natural earthquakes requires testing their generalizability across lab setti...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02200-9 |
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| _version_ | 1849389986118369280 |
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| author | Chonglang Wang Kaiwen Xia Wei Yao Chris Marone |
| author_facet | Chonglang Wang Kaiwen Xia Wei Yao Chris Marone |
| author_sort | Chonglang Wang |
| collection | DOAJ |
| description | Abstract Machine learning models can predict laboratory earthquakes using Acoustic emission, the lab equivalent of microseismicity, and changes in fault zone elastic properties during the lab seismic cycle. Applying them to natural earthquakes requires testing their generalizability across lab settings and stress conditions. Here, we show a fine-tuned convolutional neural network (CNN) model effectively transfer across different conditions. Our model employs techniques from natural language processing, including decoder techniques, to capture the relationship between AE and fault stress. We fine-tune the regression head of a deep CNN while fixing the decoder’s weights and successfully predict lab seismic events for a range of conditions. With fine-tuning, CNN models trained on one lab fault configuration predict time to failure and shear stress for another configuration at varying fault slip rates. These results demonstrate the potential of extending lab-based methods to different conditions that could eventually include tectonic earthquakes and seismic forecasting. |
| format | Article |
| id | doaj-art-b8b3ae23f26e4bcbb629576cd485b3d2 |
| institution | Kabale University |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-b8b3ae23f26e4bcbb629576cd485b3d22025-08-20T03:41:47ZengNature PortfolioCommunications Earth & Environment2662-44352025-03-016111010.1038/s43247-025-02200-9Generalizable deep learning models for predicting laboratory earthquakesChonglang Wang0Kaiwen Xia1Wei Yao2Chris Marone3State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, School of Civil Engineering, Tianjin UniversityState Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, School of Civil Engineering, Tianjin UniversityState Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, School of Civil Engineering, Tianjin UniversityDipartimento di Scienze della Terra, La Sapienza Università di RomaAbstract Machine learning models can predict laboratory earthquakes using Acoustic emission, the lab equivalent of microseismicity, and changes in fault zone elastic properties during the lab seismic cycle. Applying them to natural earthquakes requires testing their generalizability across lab settings and stress conditions. Here, we show a fine-tuned convolutional neural network (CNN) model effectively transfer across different conditions. Our model employs techniques from natural language processing, including decoder techniques, to capture the relationship between AE and fault stress. We fine-tune the regression head of a deep CNN while fixing the decoder’s weights and successfully predict lab seismic events for a range of conditions. With fine-tuning, CNN models trained on one lab fault configuration predict time to failure and shear stress for another configuration at varying fault slip rates. These results demonstrate the potential of extending lab-based methods to different conditions that could eventually include tectonic earthquakes and seismic forecasting.https://doi.org/10.1038/s43247-025-02200-9 |
| spellingShingle | Chonglang Wang Kaiwen Xia Wei Yao Chris Marone Generalizable deep learning models for predicting laboratory earthquakes Communications Earth & Environment |
| title | Generalizable deep learning models for predicting laboratory earthquakes |
| title_full | Generalizable deep learning models for predicting laboratory earthquakes |
| title_fullStr | Generalizable deep learning models for predicting laboratory earthquakes |
| title_full_unstemmed | Generalizable deep learning models for predicting laboratory earthquakes |
| title_short | Generalizable deep learning models for predicting laboratory earthquakes |
| title_sort | generalizable deep learning models for predicting laboratory earthquakes |
| url | https://doi.org/10.1038/s43247-025-02200-9 |
| work_keys_str_mv | AT chonglangwang generalizabledeeplearningmodelsforpredictinglaboratoryearthquakes AT kaiwenxia generalizabledeeplearningmodelsforpredictinglaboratoryearthquakes AT weiyao generalizabledeeplearningmodelsforpredictinglaboratoryearthquakes AT chrismarone generalizabledeeplearningmodelsforpredictinglaboratoryearthquakes |