Mineral prospectivity mapping for Porphyry-type and hydrothermal vein-type copper deposits using fuzzy analytical hierarchy process and geographic information system

Issue title: Some highlights on fuzzy systems and data mining

Guest editors: Shilei Sun, Silviu Ionita, Eva Volná, Andrey Gavrilov and Feng Liu

Article type: Research Article

Authors: Zhou, Kefa^{a; b} | Zhang, Nannan^{a; b; *}

Affiliations: [a] Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang, China | [b] Xinjiang Key Laboratory of Mineral Resources and Digital Geology, Urumqi, Xinjiang, China

Correspondence: [*] Corresponding author. Nannan Zhang, Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China. Tel.: +86 189 992 19286; Fax: +86 991 7885320; E-mail: [email protected].

Abstract: Mineral prospectivity mapping (MPM) is used to delineate target areas that most likely contain mineral deposits of a particular type. With MPM, it is difficult to properly present the geological, geophysical, geochemical and remote sensing information representing the metallogenic favorability, due to the complexity and uncertainty of ore-forming background, cause and mechanism. Fuzzy analytical hierarchy process (AHP) has been proposed to reflect various fuzzy concepts in MPM objectively. It is an extension of conventional AHP and employs fuzzy set theory to handle uncertainty. In this study, fuzzy AHP and geographic information system (GIS) were applied for mineral prospectivity mapping. Results confirm that fuzzy AHP is an effective knowledge-driven MPM method. In the MPM process, GIS serves as an auxiliary tool of fuzzy AHP, achieving the processing and integration of basic data, spatial analysis, and result presentation. The classes of 15 Alternatives layers are assigned with weights according to the studentized contrast (S(C)) in weights-of-evidence method. That is why the fuzzy AHP-based MPM could make a better prediction in this study.

Keywords: Mineral prospectivity mapping, fuzzy AHP, GIS

DOI: 10.3233/JIFS-169200

Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 3143-3153, 2016