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 |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2662-4435 |