Data-driven risk assessment of hydrothermally altered goafs in deep metal mines via unsupervised typing and nonlinear evolution.

As the "Resource Lifeline" strategy of the China National Gold Group (CNGG) extends to greater depths, its subsidiary mines encounter severe challenges, primarily characterized by tectonic fracturing, intense hydrothermal alteration, and data scarcity. Crucially, traditional empirical stability classifications function as mere "static snapshots" that fail to capture the nonlinear accumulation of hidden hazards, rendering them ineffective for early warning in complex deep environments. To bridge this gap, this study proposes a data-driven stability assessment framework that seamlessly integrates unsupervised structural typing with nonlinear phenomenological evolution. Diverging from reliance on subjective experience, this study first extracts the intrinsic geometric features of 40 typical goafs using K-means clustering to establish an objective structural typology. Subsequently, an Analytic Hierarchy Process (AHP)-Entropy combined weighting model reveals that hydro-environmental factors account for over 50% of the total risk contribution, highlighting the overwhelming statistical significance of water-rock coupling in driving instability. To quantify the continuous progression from a metastable to a high-risk state, a non-linear dynamic evolution model based on the logistic map is introduced. By incorporating the comprehensive static risk index, this model effectively characterizes the nonlinear damage amplification process. The derived critical threshold (Rc = 0.57) accurately delineates high-risk zones, providing a transparent, quantitative paradigm for prioritizing the remediation of concealed hazards in data-scarce deep mining operations.