Mining method selection based on hierarchical clustering and correspondence analysis

Selecting an optimal mining method is a complex and critical decision in underground mining, influ-enced by multiple geological, technical, and economic parameters. This study introduces a novel framework that combines Hierarchical Clustering (HC) and Correspondence Analysis (CA) to en-hance the selection process by evaluating the consistency and similarity among outcomes from both first-pass methods (UBC and Nicholas) and several multi-criteria decision-making (MCDM) tech-niques (including AHP, EDAS, PROMETHEE II, AHP-PROMETHEE, TOPSIS, and VIKOR). The proposed HC-CA approach identifies consistent conflicts among the considered mining methods and quantifies the agreement among the initial assumptions of the adopted selection procedures. A case study of a Pb-Zn deposit demonstrates that the framework can effectively detect consistent and co-occurring (i.e. conflicting) solutions, such as Cut-and-Fill Stoping, Shrinkage Stoping, and Sublevel Stoping. The results show that the adopted design criteria align more closely with the UBC selection method, compared to the Nicholas selection procedure for the considered deposit. Additionally, ap-plying the HC-CA approach to the input matrices prior to applying the MCDM methods can yield different results, compared to subjecting the MCDM output scores to the proposed framework. This integrative approach extends traditional selection procedures and links them with commonly used MCDM methodologies and unsupervised machine learning methods by enabling flexible strategy development, with the inclusion of considering mixed-mining-method scenarios tailored to the depos-it. Additionally, the approach offers improved decision support in early project stages by visualizing affinities among different assumptions and hence potentially mitigating biases during the following design stage.