A Multimodal Deep Learning Framework for Rapid Real-Time Earthquake Magnitude Classification: Applications to Earthquake Early Warning

ABSTRACT Rapidly and accurately determining whether a seismic event is small or large is crucial for seismic hazard analysis and earthquake early warning systems (EEWSs) to predict potential damage in target areas. However, the lack of prior information about the earthquake source and the class imbalance due to the limited availability of large earthquake waveforms pose significant challenges for this task. To address this issue, we applied a random sliding window technique to extent the waveform data of large earthquakes. Meanwhile, we developed a multimodal deep learning framework (MDLFrame) to distinguish between small (M < 5.5) and large (M ≥ 5.5) earthquakes using data sets from Japan and China. The MDLFrame is capable of extracting useful temporal features and spatial features from the input three-component waveforms and ground-motion parameters. The test results demonstrated that the MDLFrame outperformed traditional empirical methods and unimodal deep learning models in both timeliness and accuracy of magnitude classification. Specifically, the MDLFrame achieved an accuracy of 96.11%. To further confirm the robustness of the MDLFrame for magnitude classification, we analyzed its performance on 220 independent seismic events. The results indicated that within 3 s after the P-wave arrival, the MDLFrame achieved an accuracy of 98.68% for small earthquakes and 93.21% for large ones. Our study demonstrates that multimodal deep learning has significant potential for extensive applications in EEW and seismology.