基于深度学习的日本强震远场触发中国东北地震预测OA

Far-field earthquake triggering mechanisms represent a critical frontier in earthquake predic-tion research.To explore the spatiotemporal relationship between strong seismic activity in Japan and moderate-to-strong earthquakes in northeast China,this study develops a data-driven prediction model.Based on the USGS earthquake catalog from 1980 to 2024,and using M≥6.0 earthquakes in Japan as potential triggers,the model predicts the probability of earthquakes withM≥4occurring in northeast China within the next 60 days.A daily-resolution time series dataset is constructed,incor-porating 32-dimensional features covering earthquake statistics,spatial distribution,energy release,and aftershock sequences.Deep learning models including long short-term memory(LSTM)neural networks,attention mechanism-enhanced LSTM(Attention-LSTM),and Transformer are systemat-ically compared against traditional machine learning methods such as logistic regression and random forest.Results indicate that the Attention-LSTM model performs optimally,achieving an F1 score of 0.915 and an AUC value of 0.661,and shows significant advantages in Molchan error diagram analy-sis,a method specific to earthquake prediction evaluation.The model enables both binary classification prediction and generation of spatial probability distribution maps at 1° × 1° grid resolu-tion.This study demonstrates the potential of deep learning for revealing cross-regional far-field earthquake triggering mechanisms and provides new insights for short-to-medium-term regional seis-mic hazard assessment.