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Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping

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Abstract

Our research focuses on examining two clustering methods, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and fuzzy c-means (FCM) algorithms, to create prospectivity models for Cu mineralization in the Kerman belt, SE Iran. Traditional clustering techniques struggle with computational complexity and adaptability to large datasets, leading to a growing interest in developing improved algorithms. While numerous clustering algorithms have been utilized with promising outcomes, their performance heavily relies on user-specified parameters. The DBSCAN and FCM clustering methods were implemented to reduce the dimensions of nine attribute vectors derived from different exploration criteria. DBSCAN stands out for its ability to detect clusters with diverse shapes, finding applications in image processing, bioinformatics, and social network analysis. In contrast, traditional partitional clustering techniques face challenges in implementing non-convex clustering and may converge to a locally optimal solution. To identify indicators and potential controls on mineralization, we employed various evidence layers such as geochemical signatures, geological-structural clues, geophysical and remote sensing data. To enhance multi-element geochemical signatures in areas with Cu mineralization, we employed multifractal inverse distance weighting interpolation in conjunction with factor analysis. We transformed values of various evidence layers, including geological-structural controls and alterations, using the GIS-based fuzzy membership function MSLarge to fit within a range of 0 to 1. The Xie and Beni (VXB) index has been used in determining the optimal number of clusters for FCM-based MPM. We also utilized normalized density indices for a quantitative evaluation of the DBSCAN and FCM prospectivity maps. The evaluation results confirm the superior reliability of higher favorability classes derived from DBSCAN over those obtained from FCM in the identification of existing mineral deposits and the identification of new potential Cu mineralization zones within the study area.

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Acknowledgements

The authors would like to thank Prof. Babaie, the Editor-in-chief, for handling this manuscript. Moreover, we want to thank the Geological Survey of Iran (GSI) for providing the geospatial database used in this paper.

Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Reza Ghezelbash

  2. Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

    Mehrdad Daviran, Abbas Maghsoudi & Mahsa Hajihosseinlou

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  1. Reza Ghezelbash
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Contributions

Reza Ghezelbash: Methodology, Software, Formal analysis, Data curation, Writing – review & editing, Visualization. Mehrdad Daviran: Methodology, Software, Formal analysis, Data curation, Writing – original draft, Visualization. Abbas Maghsoudi: Resources, Supervision. Mahsa Hajihosseinlou: Writing – original draft.

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Correspondence to Reza Ghezelbash or Abbas Maghsoudi.

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Communicated by Hassan Babaie.

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Ghezelbash, R., Daviran, M., Maghsoudi, A. et al. Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping. Earth Sci Inform 18, 217 (2025). https://doi.org/10.1007/s12145-025-01708-0

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