Decoding multicomponent hydrochemical anomalies: A synergy anomaly detection model for earthquake forecasting in active tectonic zone

The intersection of the Xiaojiang Fault and the Red River Fault at the southeastern margin of the Tibetan Plateau experiences intense tectonic activity. At this intersection, frequent destructive earthquakes have induced hydrochemical variations in thermal springs. In this study, Bayesian change point analysis is applied, and a multicomponent synergy anomaly detection model is developed using five years of monitoring data (2019–2024) from two thermal springs in the region to achieve real-time forecasting of occurrence timing for M ≥ 4 earthquakes. Comprehensive analysis demonstrates that the anomaly detection model possesses reliable real-time anomaly detection capabilities. Tailored model parameters for specific hydrochemical components account for their differences in response characteristics to seismic activity. The model identifies Na+, Ca2+, Cl−, SO42−, δD, and δ18O as sensitive indicators for strong earthquake forecasting. The multicomponent synergy alarm mechanism for hydrochemistry overcomes the limitations of single-parameter methods, which significantly enhances the model’s overall performance in earthquake forecasting. The number of hydrochemical components with synchronous anomalies serves as a reliable criterion for determining alarm intensity, with higher intensity typically correlating with larger earthquake magnitudes or shorter epicentral distances.