Improving the results of the fractal model of geochemical Mineralization Probability Index Using the Gray Wolf Algorithm on the Stream Sediments Data of Sarduiyeh-Baft Area | ||
| Journal of Mining and Environment | ||
| مقاله 2، دوره 16، شماره 4، مهر و آبان 2025، صفحه 1165-1178 اصل مقاله (7.05 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.14757.2792 | ||
| نویسندگان | ||
| Kamran Mostafaei* 1؛ Mohammad Nabi Kianpour2؛ Mahyar Yousefi3؛ Meisam Saleki2 | ||
| 1Department of Mining, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 2School of Materials and Mineral Resources Engineering, Engineering Campus, Universiti Sains Malaysia (USM) | ||
| 3Faculty of Engineering, Malayer University, Malayer, Iran | ||
| چکیده | ||
| Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting optimal thresholds. This study proposes the Grey Wolf Optimizer (GWO) algorithm as a novel approach for identifying the optimal boundary between anomalies and background. Stream sediment geochemical data from a copper-mineralized area of the Sarduiyeh-Baft sheets in southeast Iran were utilized for analysis. The Geochemical Mineralization Probability Index (GMPI) was first calculated for Cu-Au, Mo-As, Pb-Zn, and porphyry distributions. Subsequently, fractal methods were used to identify anomalous populations within each GMPI. The GWO algorithm was then applied to these distributions to determine the optimal thresholds. Risk analysis, calculated as the ratio of covered copper occurrences to the covered area, revealed superior reliability for the GWO-derived limit compared to those obtained using fractal methods. For porphyry GMPI values, while the fractal reliability indices are 0.127, 0.44, and 0.5, the GWO limit achieved a value of 0.66. Risk analysis for Cu-Au distribution also caused more desired result for GWO limit rather that fractal ones. This demonstrates the enhanced performance and superior reliability of the GWO algorithm for optimizing anomaly detection thresholds in GMPI data. | ||
| کلیدواژهها | ||
| Geochemical data؛ Stream sediment؛ Gray Wolf Optimizer؛ Anomaly Separation | ||
| مراجع | ||
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