Real-time discrimination of earthquake signals by integrating artificial intelligence technology into IoT devices - Zhi Geng; Yanfei Wang; Wenyong Pan; Caixia Yu; Zhijing Bai; Hongzhou Zhang (Communications Earth & Environment, 2025)
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.