Abstract:
To enhance the capability for monthly-scale regional strong earthquake risk prediction, this paper introduces a probabilistic seismic hazard prediction model based on Bayesian network structure learning. Initially, a series of predictive indicators, serving as the nodes of the Bayesian network, are derived from the earthquake catalog. Subsequently, the thresholds for each node and the directed connections among nodes are determined using swarm intelligence algorithms. Ultimately, through parameter estimation, the target node outputs the probability of M
W 5.0+ strong earthquakes occurring in the target region within the next month. Experimental results indicate that the model achieves an average prediction efficiency metric of 0.783, and validation via the Molchan test confirms its significant effectiveness, demonstrating the model's capacity to comprehensively explore the latent causal relationships between seismic precursors and strong earthquakes.
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