Exploration
Mohammadjafar Mohammadzadeh; Majid Mahboubiaghdam; Moharram Jahangiri; Aynur Nasseri
Abstract
Most machine learning-monitored algorithms used to create mineral potential prediction maps require noise-free data to achieve high performance and reliable results. Unsupervised clustering methods are highly effective for uncovering a dataset’s hidden structures. Therefore, this study attempts ...
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Most machine learning-monitored algorithms used to create mineral potential prediction maps require noise-free data to achieve high performance and reliable results. Unsupervised clustering methods are highly effective for uncovering a dataset’s hidden structures. Therefore, this study attempts a combination of supervised and unsupervised methods employing training and testing data to generate a highly accurate potential map of the Sonajil copper-gold deposit located in the NW of Iran. Here, a semi-supervised Bayesian algorithm is used to map the mineral landscape. Initially, ten raster layers of exploratory features are prepared. Then based on the copper concentration, 27 exploratory drilled boreholes are divided into four classes, C1 to C4, and from each class, two boreholes are selected, and 100-meter buffering is performed around these boreholes to extract 1113 training data based on the behavioral pattern of boreholes and surface samples. Subsequently, the existing data is clustered using the FCM method, and the total dataset and the clustering data are entered into the Bayesian algorithm to evaluate the accuracy of the Bayesian classifier method across five distinct clusters. The results show increased average accuracy when using clustered data instead of whole data for MPM mapping. Notably, the Bayesian semi-supervised algorithm achieved an impressive accuracy rate of 96% when cluster five data is excluded. To validate the Bayesian semi-supervised method, boreholes data that is not used in training were employed, which confirm the credibility of generated MPM. Overall results highlight the value of the Bayesian semi-supervised algorithm in improving the accuracy and reliability of mineral prospectivity mapping via the application of the FCM clustering method that efficiently organize the data, enabling the Bayesian algorithm to evaluate the accuracy of the Bayesian classifier method across different clusters and providing a successful optimal result in detecting blind ores in areas without exploratory boreholes and delineating more mineralization targets in the Sonajil and adjoining areas.
