Prediction of Mining-Induced Subsidence Using 3D Numerical Modeling Techniques: A Critical Review on Advancements, Applications, Challenges and Opportunities

Mining-induced subsidence remains a critical concern for mine safety, issues related to surface and underground infrastructure, mine planning and environmental risk. This paper presents a focused critical review that is specifically devoted to mining-induced subsidence prediction using 3D numerical modeling techniques, with its advancements, applications, challenges, and opportunities. It first traces the evolution from empirical, graphical, and influence function approaches to analytical approaches, which include numerical methods and probabilistic methods, and then to statistical and deep-learning-based models for subsidence predictions, highlighting their capabilities and limitations in complex geological conditions. These methods are included to establish the historical and comparative context, while the central focus of the review remains the development, application, calibration, validation, limitations and future use of 3D numerical modeling for mining-induced subsidence prediction. The review then concentrates on 3D numerical frameworks, including continuum (FDM, FEM, and BEM), discontinuum (DEM and DFN-based) and hybrid methods (FEM–DEM/BEM–FEM), covering their historical development, application in coal and metal mines, and strengths for simulating caving, time-dependent behavior and multi-seam conditions. Particular attention is given to geotechnical inputs (in situ tests, inverse analysis, and rock-mass classification); constitutive modeling, including creep and damage; and data assimilation, calibration and validation, using InSAR/MTInSAR, GNSS, and levelling data. The review synthesizes key challenges like geological heterogeneity, uncertainty regarding rock-mass parameters and discontinuities, scale effects, computational demands and human/operational factors and outlines opportunities where 3D numerical models, coupled with probabilistic tools and machine learning techniques which can enhance hazard prediction, infrastructure and water-management planning, regulatory compliance, and community land-use decisions, can be deployed. Overall, the paper concludes that InSAR-calibrated 3D numerical modeling, embedded in transparent calibration/validation workflows, currently provides the most robust foundation for safer and more sustainable mining operations.