Maden Potansiyeli Tahmin Yöntemlerine Genel Bir Bakış

Mining prospectivity analyses enhance success rates and reduce costs by directing exploration efforts toward areas with the highest discovery potential. The subjectivity of traditional analysis has been addressed since the 1960s through the use of statistical techniques. The development of geographic information systems (GIS) technology in the 1980s and advancements in machine learning and data science in the 2000s have made the process more objective and effective. These analyses are conducted using two main approaches: knowledge-driven and data-driven methods. Knowledge-driven systems mimic expert reasoning and rely on deposit models or mineral system approaches. The weight-of-evidence method is the most commonly used among these systems. GIS is used to integrate geoscientific data, to calculate positive and negative weights, and to update probabilities using Bayes' theorem. Other knowledge-driven approaches include analytic hierarchy process (AHP) and fuzzy logic. Data-driven systems learn directly from data without relying on predefined models or hypotheses. In addition to statistical methods (e.g., logistic regression analysis), machine learning algorithms—such as support vector machines, artificial neural networks, and decision trees—are highly effective in identifying patterns within complex datasets. These methods make exploration processes more systematic and predictive. However, their success depends on data quality and the effectiveness of the selected models. Therefore, collecting accurate data and optimizing analytical processes have great importance.