Decoding multicomponent hydrochemical anomalies: a synergistic detection model for earthquake forecasting

Abstract

The intersection of the Xiaojiang Fault and the Red River Fault at the southeastern margin of the Tibetan Plateau experiences intense tectonic activity, where repeated earthquakes cause variations in thermal spring hydrochemistry. This study applies Bayesian change point analysis and develops a multicomponent synergistic anomaly detection model, using monitoring data from the Qujiang (5 years, 2019–2024) and Wana (2.5 years, 2021–2024) springs in this region to facilitate the real-time forecasting of the timing for M≥4 earthquakes. A 45-day response time threshold is established as the optimal period for capturing hydrochemical precursors in this region. With parameters optimized for individual components based on their distinct geochemical responses to seismic stress, the model features adaptive alarm criteria that ensure reliable real-time detection and enhanced adaptability. At the Qujiang site, the model achieved 21 effective alarms for 22 earthquake events with 1 miss and 8 false alarms, yielding a probability of detection (POD) of 0.95 and a threat score (TS) of 0.70. At the Wana site, the model generated 10 accurate alarms for 12 events with 2 misses and 5 false alarms, resulting in a POD of 0.83 and a TS of 0.59. The model identified pre-earthquake anomalies in Na+, Ca2+, Cl−, SO42-, δD, and δ18O, with TS ≥0.50. These components can serve as sensitive indicators for strong earthquake forecasting. The multicomponent synergistic alarm mechanism overcomes the limitations of single-parameter methods, where the number of hydrochemical components with synchronous anomalies serves as a reliable criterion for forecasting. A higher count of anomalous components typically correlates with larger earthquake magnitudes or shorter epicentral distances. This model has the potential to be applied to thermal spring monitoring across diverse active tectonic regions through targeted parameter optimisation, offering a valuable reference for earthquake forecasting.