MEANET: MAGNITUDE ESTIMATION VIA PHYSICS-BASED FEATURES TIME SERIES, AND NEURAL NETWORKS

The traditional magnitude estimation method, which establishes a linear relationship between a single warning parameter and the magnitude, exhibits considerable scatter and underestimation. Additionally, the extraction of features from raw waveforms by a deep-learning network is a black box. To provide more robust magnitude estimation and to construct deep-learning network with an interpretable input, in light of deep-learning and earthquake rupture physics, we establish a Magnitude Estimation network model (MEANet) via the physics-based features time series, an Attention mechanism, and neural Networks. We use events with 4≤M≤7.5 that occur in Japan and the Sichuan-Yunnan region, China, to train and validate MEANet, then use MEANet to test additional events. Our results show that MEANet has more robust magnitude estimation than the traditional τ_c and P_d methods, with a standard deviation of error of ±0.25 magnitude units at a single station with a 3 s P-wave time window. Within 10 s after the first station is triggered, based on the weighted average of the triggered stations, MEANet provides robust magnitude estimation without underestimation for events with 4≤M≤7.5. Our finding implies that the final magnitude is to some degree deterministic by the combination of deep-learning and physics-based features. Meanwhile, MEANet might have potential in earthquake early warning.