Geochemical modeling of orogenic gold deposit using PCANN hybrid method in the Alut, Kurdistan province, Iran

Mohammadzadeh, M J; Nasseri, Aynur

doi:10.1016/j.jafrearsci.2017.11.038

Cited by 6 publications

(1 citation statement)

References 41 publications

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“…There are no fixed rules or criteria for model selection, but the model will be selected based on the mentioned factors and the conditions of the region. Commonly used MPM methods belonging to data-driven models include weights of evidence (Nykänen et al 2008), fuzzy weights of evidence (Zhang et al 2016), boosted weights of evidence (Cheng 2015) ,support vector machine (Ghezelbash et al 2021;Tao et al 2022), random forest (Carranza and Laborte 2016;Gao et al 2016;McKay and Harris 2016), logistic regression and its variants (Zhang et al 2018a;Zhang et al 2018b), neural networks (Chen et al 2022;Li et al 2021;Mohammadzadeh and Nasseri 2018), Bayesian networks (Porwal et al 2006), evidence belief functions (Liu et al 2015), to name but just a few. In recent years, unsupervised learning models have been used to map potential mineral areas to deal with situations where dispersed exploratory information exists.…”

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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