A Multi-Parameter Monitoring Framework for Earthquake Early Warning Based on the Encrypted Chain Fracture Model

This paper proposes a multi-parameter monitoring framework for earthquake early warning based on the Encrypted Chain Fracture Model. The model derives mathematical expressions for earthquake recurrence interval and magnitude from the deterministic relationship between stress accumulation and critical fracture along plate boundaries. The core equations are recurrence interval T = S_crit / α and magnitude M = (2/3)·log₁₀(η·S_crit·L·W) − 6.0, where α is the encrypted chain stress accumulation rate, S_crit is the fracture threshold, and L and W are the length and width of the fault zone. Using global GPS observations, historical earthquake catalogs, and geological survey data, hindcasting validation was performed on seven landmark large earthquakes between 1906 and 2023, with magnitude deviations not exceeding 0.1 magnitude units. The model has been applied to short-term deterministic prediction for 2026, where two of the eight predicted plate-boundary systems experienced large earthquakes within the forecast window following model registration, providing preliminary independent validation. Based on the physical mechanism of the model, this paper systematically derives seven independent earthquake precursor signals—accelerated stress accumulation, four-quadrant deformation rate pattern, spatiotemporal evolution of microseismicity, geomagnetic deflection anomaly, electric field pulse anomaly, magnetic field gradient anomaly, and anomalous free energy concentration—each with clearly specified physical origin, observational method, and early warning criterion. A rigorous retrospective evaluation against instrument records from historical earthquakes is conducted, identifying which signals have strong quantitative support, which have partial or mixed support, and which remain forward predictions awaiting dedicated analysis. A quantitative detectability analysis is performed for each signal, estimating the theoretical signal-to-noise ratio under typical instrumental noise levels and identifying the conditions under which each signal is detectable with current technology. The probability of signal occurrence as a function of the stress ratio R is estimated from available retrospective cases, providing an empirical basis for calibrating warning thresholds. The correlation structure among the seven signals is analyzed, and a joint detection framework is constructed that exploits their physical interdependencies. A multi-parameter hierarchical early warning system is designed with three warning levels, a complete six-step operational workflow from risk assessment to warning issuance, and explicit treatment of signal correlations. A cost-effectiveness framework for monitoring network deployment is provided, and the relationship of this framework to existing P-wave-based early warning systems and to machine learning approaches is discussed. A detailed flagship validation case study for the Nankai Trough subduction system, Japan, demonstrates the complete pathway from existing monitoring infrastructure to operational signal detection. Monitoring network deployment recommendations, noise discrimination strategies for each signal type, and an explicit statement of the framework's limitations provide the practical foundation for operational implementation.