A Framework for Integrating Mineralogical and Geochemical Data in Ore Deposit Models

Thesis Abstract

The accurate characterization of ore deposits critically depends on the integration of mineralogical and geochemical data, yet existing models often underutilize the synergistic potential of these datasets, leading to incomplete or imprecise interpretations of deposit genesis and mineralization processes. This study aims to develop a comprehensive framework that systematically combines mineralogical and geochemical information to enhance ore deposit modeling accuracy and predictive capability. The specific objectives are to identify the key mineralogical and geochemical parameters that influence ore deposit characteristics, to establish analytical procedures for effective data integration, and to validate the proposed framework through case studies on known deposits within the mineral-rich belt of the Pacific Northwest. Employing a mixed-methods research design, the study integrates both quantitative and qualitative approaches. The population comprises mineralogical and geochemical datasets sourced from 15 well-documented ore deposits, with a sample size of 120 core samples randomly selected to ensure statistical representativeness. Mineralogical data were obtained through detailed petrographic analysis and electron probe microanalysis (EPMA), while geochemical data were gathered via inductively coupled plasma mass spectrometry (ICP-MS). Data collection instruments included high-resolution scanning electron microscopes (SEM), X-ray diffraction (XRD), and geochemical analytical protocols calibrated according to International Standard Methods. The validity and reliability of the analytical instruments and procedures were assured through calibrated standards, replicate analyses, and inter-laboratory comparisons, establishing a coefficient of variation below 5%. The analytical framework incorporates multivariate statistical techniques, notably principal component analysis (PCA) and hierarchical clustering, to discern the underlying relationships and association patterns within the mineralogical and geochemical datasets. Regression analysis, specifically multiple linear regression, is employed to model the influence of mineralogical features on geochemical signatures, while thematic analysis guides the qualitative interpretation of mineral paragenesis. The conceptual basis of the framework is rooted in the theories of mineral-silicate associations and geostatistical modeling, with a focus on the theory of paragenesis as a foundation for understanding mineralization sequences. The anticipated findings suggest that the integrated framework will delineate distinct mineralogical-geochemical signatures associated with specific deposit types and developmental stages, enabling more precise predictive models for undiscovered deposits. The study expects to identify key mineralogical indicators that significantly correlate with element variance, enhancing the predictive accuracy of geochemical anomalies. This integration will facilitate improved spatial modeling of mineralization zones and better understanding of mineral-fluid interactions throughout ore formation. Contributing novel insights into mineral deposit modeling, this research advances the current knowledge by demonstrating the feasibility and benefits of a systematic, integrated approach, bridging a critical gap between mineralogical pore-scale information and geochemical bulk-scale data. The framework’s applicability extends to mineral exploration, resource estimation, and environmental impact assessments, providing a robust tool for geoscientists. The main conclusion emphasizes that successful integration of mineralogical and geochemical data enhances the reliability of ore deposit models, supporting more targeted exploration strategies and resource management. The study recommends the adoption of the proposed framework in regional mineral assessment programs and advocates for further research into its adaptation to other deposit types, such as volcanic-hosted massive sulfides (VHMS) and epithermal deposits. Future investigations should also explore advanced machine-learning algorithms to automate data integration processes, further refining predictive capabilities in ore deposit modeling.