Development of Analytical Models for Estimating Ore Quantities Using Geological and Geophysical Data

Abstract

Eyab A. Alshehab

Accurate estimation of ore quantities is pivotal for the efficiency and economic viability of mining operations. Recent advancements in data analytics have facilitated the development of sophisticated analytical models that leverage geological and geophysical data to predict ore deposits. This research critically evaluates the effectiveness of these models by comparing their predictions with actual extraction data and through a comprehensive survey of industry experts. The study employs several well-established statistical and machine learning models documented in the literature, such as those explored by who demonstrated the use of regression analysis in mineral prediction, and who applied machine learning techniques in geological datasets [1]. These models were selected for their proven capabilities in handling complex data structures and their previous successful applications in similar contexts [2].

We conducted a detailed comparison of model outputs with data from actual mining sites where the quantities of extracted ore were measured. This empirical validation approach follows the methodology suggested by who emphasized the importance of real-world data validation in predictive model assessments. Concurrently, a survey was distributed among geology and mining professionals, designed to capture qualitative and quantitative evaluations of the models' performance, reliability, and practical utility in operational settings. The results indicate a significant correlation between the models' predictions and the actual data, with some discrepancies that highlight areas for model improvement. The survey responses, analyzed through statistical methods recommended by further supported the models' utility while suggesting enhancements for increased accuracy [3].

This paper contributes to the mining sciences by confirming the potential of data-driven models in ore estimation and by providing a methodological framework for their empirical validation. It also outlines critical areas for future research, particularly in model optimization and the integration of emerging geophysical data types, paving the way for more precise and economically feasible mining operations.

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