Predictive Finite-Time Multi-Switching Synchronisation of Fractional-Order Hyperchaotic Systems for Seismic Signal Analysis and Earthquake Stability Monitoring

Abstract

Earthquake systems are highly nonlinear, memory-dependent and characterised by hidden instability, making conventional deterministic and statistical monitoring approaches inadequate for capturing irregular seismic behaviour. This study proposes an Predictive finite-time multi-switching synchronisation framework for fractional-order hyperchaotic systems applied to seismic signal analysis and earthquake stability monitoring. The framework combines memristive, hidden-attractor and neural hyperchaotic dynamics within a multi-drive multi-response architecture to represent memory effects, concealed instability and adaptive nonlinear signal behaviour. The Caputo fractional derivative is employed to model hereditary geophysical characteristics associated with tectonic loading, stress accumulation and delayed crustal response. Multi-switching synchronisation errors are formulated explicitly, and adaptive finite-time controllers are designed to guarantee rapid convergence despite nonlinear uncertainty and parameter variation. Lyapunov stability theory is used to establish sufficient conditions for finite-time convergence of the synchronisation errors. Numerical implementation is carried out in MATLAB, where phase portraits, time-series responses, synchronisation error trajectories, error norms, drive-response tracking and bifurcation behaviour are analysed. The simulation results indicate that the proposed method achieves stable and rapid synchronisation, while preserving the complex nonlinear features required for realistic seismic modelling. The framework also demonstrates potential for identifying abnormal transitions, interpreting hidden fault activation and monitoring changes in earthquake-related dynamical stability. Overall, the study provides a unified nonlinear modelling and control approach that integrates fractional calculus, hyperchaos and adaptive synchronisation for improved seismic anomaly detection and earthquake stability assessment. It further establishes a computational foundation for future validation using recorded seismic datasets, real-time monitoring platforms and experimentally calibrated geophysical parameter values in practice.