Commercial Surveys

Commercial Surveys

plication of Machine Learning to Improve the Efficiency of Mineral Exploration: A Case Study of Geochemical Anomaly Separation in the Maraki Area, Hormozgan Province, Iran

Document Type : Original Article

Authors
Mahdis Satari 1
Golnaz Jozanikohan 2
1 , M.Sc. Student in Mining Engineering (Mineral Exploration), School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2 Assistant Professor, Ph.D. in Mining Engineering (Mineral Exploration), School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.
10.22034/bs.2026.2089400.3242
Abstract
The increasing costs and uncertainties associated with the early stages of mineral exploration highlight the need for advanced analytical approaches to improve the efficiency of exploration processes. This study investigates the application of machine learning algorithms to enhance mineral exploration efficiency through the separation of geochemical anomalies. The Maraki area in Hormozgan Province, Iran, was selected as the case study, and regional geochemical data were analyzed using a combination of statistical methods and machine learning models. Initially, descriptive statistical and correlation analyses were conducted to examine the characteristics of the dataset and identify relationships among geochemical elements. Subsequently, classical statistical approaches, including the standard deviation method and the Median Absolute Deviation (MAD) method, were applied to distinguish background values from anomalous concentrations. In the next stage, the Random Forest algorithm was employed as an effective machine learning technique to identify complex patterns and nonlinear relationships among geochemical elements. The results indicate that the Random Forest model outperforms traditional statistical methods in identifying areas with high mineralization potential. The probability maps generated by the model also demonstrate strong spatial correspondence with the geological and structural features of the study area. Overall, the findings suggest that integrating machine learning techniques with conventional geological and geochemical analyses can significantly improve the accuracy of exploration data interpretation, reduce uncertainty during the early stages of exploration, and enhance the economic efficiency of mineral exploration activities. These results highlight the potential of data-driven approaches to improve decision-making and planning in mineral exploration.
Keywords
Machine Learning/ Random Forest/ Geochemical Anomaly/ Mineral Exploration/ Exploration Efficiency/ Maraki
Hormozgan
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Commercial Surveys
Volume 23, Issue 134 - Serial Number 134
November and December 2026
Pages 24-38

  • Receive Date 23 May 2026
  • Revise Date 30 May 2026
  • Accept Date 02 June 2026