Mineral prospectivity mapping of Cu-Au by integrating 					AHP technique with ARAS and WASPAS models in the Sonajil area, 					E-Azerbaijan

Mohammadzadeh, M J; Nasseri, Aynur; Mahboubiaghdam, Majid; Jahangiri, Moharram

doi:10.1127/zdgg/2021/0256

Cited by 1 publication

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various factors, including lithology, are important exploratory guides that affect the distribution pattern of mineralization in the area. The main lithological units of the Sonajil include andesite to hornblende-andesite, porphyritic and mega-porphyritic andesite, and intrusive granitoid [28]. The porphyritic andesite is pronounced with phyllic alteration.…”

Section: • Geological Layermentioning

confidence: 99%

“…The models are suitable for well-explored areas in order to detail exploration surveys and c) Hybrid models which are considered based on both expert's opinions and locations of known mineralization occurrences in the area to assign weight to the evidence criteria. Some of the most important knowledge-driven methods are index overlay [5,20], fuzzy logic technique [26,29,31], wildcat method [7]and multi-criteria decision making methods [1,28]. In recent decades, various data-driven techniques developed and led to advances in the development of MPM such as neural networks(NN) [3,32,38,40], Bayesian classifiers [35], support vector machines (SVM) [12,19,54,58], and random forest method [8,39,44].…”

Section: Introductionmentioning

confidence: 99%

“…In a subsequent event, the Sonajil range experienced another intrusion, known as "incheh", which comprises diorite to quartz-monzodiorite composition. This intrusion is visible in the northern part of area, exhibiting no significant alteration or mineralization [21,28,30]. The geological map of the Sonajil range is illustrated in Figure 1.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A New Frontier in Mineral Exploration: Hybrid Machine Learning and Bat Metaheuristic Algorithm for Cu-Au Mineral Prospecting in Sonajil area, E-Azerbaijan

Mohammadzadeh

Mahboubiaghdam

Nasseri

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Modeling mineral prospectivity is a challenging strategy in characterization of blind ore zones and mineral prospecting. Therefore, the application of advanced spatial modeling techniques and machine learning algorithms is important in exploration pattern recognition. In this study, a combination of a support vector machine (SVM) and the bat algorithm (BA) has been employed to improve the classification and produce an optimal prospectivity map of porphyry copper-gold deposit in the Sonajil area, NW of Iran. In the proposed model, BA was used to optimize the SVM parameters. In data preparation, evidence layers of 10 exploratory features (geological, geochemical, remote sensing and boreholes) were prepared. Then based on 100-meter buffering around boreholes, the data (training and testing) was selected. For mapping mineral prospectivity in the area, the results of two predictive single SVM and hybrid SVM-BA models were compared. Both models were trained by way of predictor maps and then the models performance were evaluated by confusion matrix and receiver operating characteristic (ROC) curve. The results showed that both predictive models had an obvious optimization effect on recognizing the Cu-Au mineralization pattern but the hybrid model had higher accuracy in anomalous zone enhancement, with ROC values more than 0.8, indicating this optimization was successful and the selected optimal model is the best predictor for mineral prospectivity in the area. The delineated targets are also in accordance with the characteristics of the area metallogenic system showing that the established hybrid model is an effective tool in mineral prospectivity mapping.

show abstract

Section: • Geological Layermentioning

confidence: 99%