Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices
Abstract The real-time detection and analysis of seismic signals is crucial in geophysics research, especially when it comes to monitoring catastrophic events. We present an evolutionary deep learning method that yields a model named MCU-Quake. This model encodes the discrimination process as a sing...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02003-y |
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| _version_ | 1832571267726704640 |
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| author | Zhi Geng Yanfei Wang Wenyong Pan Caixia Yu Zhijing Bai Hongzhou Zhang |
| author_facet | Zhi Geng Yanfei Wang Wenyong Pan Caixia Yu Zhijing Bai Hongzhou Zhang |
| author_sort | Zhi Geng |
| collection | DOAJ |
| description | Abstract The real-time detection and analysis of seismic signals is crucial in geophysics research, especially when it comes to monitoring catastrophic events. We present an evolutionary deep learning method that yields a model named MCU-Quake. This model encodes the discrimination process as a single numerical value, offering interpretability with only 2693 parameters. Trained on raw seismic waveforms from Utah, USA, MCU-Quake demonstrates its generalization capability across a global natural earthquake dataset. Notably, the model effectively identifies typical explosions during the Russia-Ukraine war in Europe. The knowledge to discriminate between ambient noise, explosions and natural earthquakes can be represented by values of −5.01 (std: 1.14), 1.96 (std: 0.36), 1.01 (std: 0.49), respectively. The model can be deployed on Internet of Things (IoT) devices, including most microcontrollers, which are constrained by limited computational resources (kilo-bytes of memory) and energy consumption (micro-Watts). The results indicate the prospect of on-site missions of artificial intelligent sensors. |
| format | Article |
| id | doaj-art-cb2f3345daf64c5fab7526b5377a9ff6 |
| institution | Kabale University |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-cb2f3345daf64c5fab7526b5377a9ff62025-02-02T12:44:00ZengNature PortfolioCommunications Earth & Environment2662-44352025-01-016111210.1038/s43247-025-02003-yReal-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devicesZhi Geng0Yanfei Wang1Wenyong Pan2Caixia Yu3Zhijing Bai4Hongzhou Zhang5Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of SciencesKey Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of SciencesKey Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of SciencesKey Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of SciencesKey Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of SciencesKey Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of SciencesAbstract The real-time detection and analysis of seismic signals is crucial in geophysics research, especially when it comes to monitoring catastrophic events. We present an evolutionary deep learning method that yields a model named MCU-Quake. This model encodes the discrimination process as a single numerical value, offering interpretability with only 2693 parameters. Trained on raw seismic waveforms from Utah, USA, MCU-Quake demonstrates its generalization capability across a global natural earthquake dataset. Notably, the model effectively identifies typical explosions during the Russia-Ukraine war in Europe. The knowledge to discriminate between ambient noise, explosions and natural earthquakes can be represented by values of −5.01 (std: 1.14), 1.96 (std: 0.36), 1.01 (std: 0.49), respectively. The model can be deployed on Internet of Things (IoT) devices, including most microcontrollers, which are constrained by limited computational resources (kilo-bytes of memory) and energy consumption (micro-Watts). The results indicate the prospect of on-site missions of artificial intelligent sensors.https://doi.org/10.1038/s43247-025-02003-y |
| spellingShingle | Zhi Geng Yanfei Wang Wenyong Pan Caixia Yu Zhijing Bai Hongzhou Zhang Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices Communications Earth & Environment |
| title | Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices |
| title_full | Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices |
| title_fullStr | Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices |
| title_full_unstemmed | Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices |
| title_short | Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices |
| title_sort | real time discrimination of earthquake signals by integrating artificial intelligence technology into iot devices |
| url | https://doi.org/10.1038/s43247-025-02003-y |
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