Applying Data-Driven-Based Logistic Function and Prediction-Area Plot to Map Mineral Prospectivity in the Qinling Orogenic Belt, Central China

• Published in: Minerals (Basel) Vol. 12; no. 10; p. 1287
• Main Authors: Bai, Hongyang, Cao, Yuan, Zhang, Heng, Wang, Wenfeng, Jiang, Chaojun, Yang, Yongguo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Bias; Big Data; concentration-area; Expected values; Exploration; Fault lines; Fractals; Fuzzy sets; Geology; Gold; logistic function; Logistics; Metallogenesis; Mineral deposits; mineral prospectivity mapping; Mineralization; Minerals; Neural networks; orogenic Au; Orogeny; prediction-area; Predictions; Scientific imaging; Sediments; China
• ISSN: 2075-163X; 2075-163X
• DOI: 10.3390/min12101287

This study combines data-driven-based logistic functions with prediction–area (P–A) plot for delineating target areas of orogenic Au deposits in the eastern margin of the Qinling metallogenic belt, central China. First, appropriate geological and geochemical factors were identified, optimized, and transformed into a series of fuzzy numbers with a range of 0–1 through a data-driven-based logistic function in order to determine the evidence layer for prospecting orogenic Au. In addition, the P–A plot was derived on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme, in the process of which acidic magmatic activity proved to be the most important factor of ore-controlling. Moreover, to further prove the advantages of this method, a traditional linear knowledge-driven approach was carried out for comparative purposes. Finally, based on concentration–area (C–A) fractal theory, the fractal thresholds were determined and a mineral prospecting map was generated. The obtained prediction map consisted of high, medium, low, and weak metallogenic potential areas, accounting for 2.5%, 16.1%, 38.4%, and 43% of the study area, containing 2, 3, 1, and 0 of the 6 known mine occurrences contained, respectively. The P–A plot indicated that the result predicted 83% of Au deposits with 17% of the area, confirming the joint application of the data-driven-based logistic function and P–A plot to be a simple, effective, and low-cost method for mineral prospectivity mapping, that can be a guidance for further work in the study area.