Improved index overlay mineral potential modeling in brown- and green-fields exploration using geochemical, geological and remote sensing data

Aryafar, Ahmad; Roshanravan, Bijan

doi:10.1007/s12145-020-00509-x

Cited by 4 publications

(1 citation statement)

References 62 publications

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“…The purpose of these models (e.g., C-means) is to identify the underlying distribution of predictor variables and further use them to guide MPM (Abedi et al 2013b;Jahangiri et al 2018;Wang et al 2020). The major techniques of the knowledge-driven approach include the Boolean logic (Zaidi et al 2015), index overlay (Aryafar and Roshanravan 2020) , Dempster-Shafer belief theory (Carranza et al 2008) , fuzzy logic (Abedi et al 2013a), wildcat mapping (Carranza and Hale 2002), outranking methods (Abedi et al 2013a), and data envelopment analysis (Hosseini et al 2015), self-organizing map (SOM), and K-means clustering (Zuo 2017).…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive approaches for Cu-Au mineral prospectivity map in theSonajil, NW of Iran: An improvement by Bayesian semi-supervised algorithm

Mohammadjafar

Majid

Jahangiri

et al. 2023

Preprint

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Most machine learning monitored algorithms used in the preparation of mineral potential prediction maps, require noise-free data to achieve high-performance and reliable results. To discover the hidden structures of a data set, unsupervised clustering methods are highly efficient. Therefore, in this paper, a combination of supervised and unsupervised methods was attempted using training and testing data to prepare a potential map with high accuracy in the Sonajil copper-gold deposit, located in NW of Iran. Here a semi-supervised Bayesian algorithm utilized to map the mineral landscape. At First, raster layers of 10 exploratory features were prepared. Then, 27 exploratory boreholes drilled were divided into 4 classes C1 to C4 based on the copper concentration, and from each class, two boreholes were selected and 100-meter buffering was performed around these boreholes to extract 1113 training data. Then, the existing data were clustered by the FCM method and the whole data and clustering data were entered into the Bayesian algorithm to evaluate the accuracy of Bayesian classifier method in 5 different clusters. The results show an increase in the average accuracy while using the clustered data instead of whole data for MPM mapping. Also, in case of not using cluster 5 data, it has the highest accuracy of 96%. To validate the Bayesian semi-supervised method, boreholes that were not used in data training were employed. Overall results based on the classification of boreholes and prepared potential map indicate accuracy of the Bayesian semi-supervised method as an optimal result for further exploration and delimiting more mineralization targets.

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