Bayesian Network Inference for Low-Magnitude Nonnatural Seismic Event Discrimination

Abstract In response to the gaps in understanding the causal relationship between seismic waveform features and the types of seismic events, this research is focused on seismic events of low magnitude (ML≤3.0) in the North China region. Using the Bayesian network theory, we conduct an analysis to infer event types for natural earthquakes, artificial explosions, and mining collapses, and the outcomes achieved notable efficacy for the discrimination of seismic events. Through the analysis of seismic waveforms from 1818 events, we systematically extracted and quantified 55 features in temporal, spectral, and energy domains, which were then recoded as node variables for subsequent analysis. The new data set was subject to select nodes with strong associations to the node type. Subsequently, Bayesian network topologies were constructed using three different algorithms to reconstruct the custom network, calculating posterior probabilities and marginal probabilities. Simultaneously, an extensive evaluation with precision–recall curves of the network structure was carried out, encompassing accuracy, precision, recall, and F1-score. Ultimately, sensitivity analysis was performed on each node to reveal the extent of the influence of node variations on the inference of the node type. The findings showed that the sensitivity of discrimination of seismic events was notably high for several features, including high-frequency P/S spectral ratio values (11 to ∼20 Hz), central frequency, dominant frequency, average frequency, rise and decay average frequency, the real part of the complex cepstral coefficients, peak ground acceleration, and zero crossing. In the classification of natural earthquakes, artificial explosions, and mining collapses, it was observed that the probability of mining collapses was maximized when peak ground acceleration was less than 1526.08, and concurrently, the P/S spectral ratio (11 to ∼20 Hz) fell within the range of −0.25 to −0.02.