Mineral prospectivity mapping with weights of evidence and fuzzy logic methods

Abstract

Abstract. Knowledge-and data-driven approaches are two major methods used to integrate various evidential maps for mineral prospectivity mapping (MPM). Geological maps, geochemical samples and data from known gold deposits were collected in the western Junggar area, Xinjiang Province. The geological and a spatial database for geological and mineral occurrences were constructed for the studied region. A weights-of-evidence model and a fuzzy logic model were employed for MPM, and the results were compared. Results indicate that favorable sedimentary rocks, fault density, fault distance and concentration of Au were the primary factors affecting Au mineralization. Arsenic (AS), Stibium (Sb), fault direction, quartz veins and intrusive rocks were secondary factors affecting Au mineralization. Conditional independence exerted a major influence on the weights-of-evidence model. However, posterior probability would be very high if the conditional independence was disregarded, which impaired results. Combining the quantification results provided by weights-of-evidence and the fuzzy membership values determined by expert knowledge, the mineral prospectivity mapping according to the fuzzy logic method was proved to be valid. For the study area, which had a large number of deposits, data-driven approaches for MPM are generally considered to be appropriate. However, if sufficient data are not collected, the knowledge-driven approaches, for example, the fuzzy logic method used in the present study, usually achieves a better result.

Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China

“…The western Junggar area is located on the western margin of the Junggar Basin, northern Xinjiang, China. In terms of administrative division, the western Junggar area belongs to Tacheng prefecture, Ili Kazakh Autonomous Prefecture (Zhang and Zhou 2015;Liu et al 2017). It extends to Sawuer Mountain in the north, Ebinur Lake in the south, Buerkesidai-Karamay in the east, and Omin-Tuoli-Alataw pass in the west (Fig.…”

“…In MPM, different evidential layers are extracted from known deposit-types as training input data (Bonham-Carter 1994) in order to find a spatial correlation between known deposits and various layers based on numerous computational algorithms (Carranza 2008). The integration of different evidential layers and the identification of favorable areas are key tasks in the MPM process (Zuo et al 2014;Zhang and Zhou 2015), and mathematical models play an important role in this procedure.In general, mathematical models for MPM can be categorized into knowledge-or data-driven constructs, Communicated by: H. A. Babaie although hybrid models also exist (Cheng and Wu 2017). In knowledge-driven techniques, few known mineral deposits are predicted in areas of interest; the approach is commonly used in poorly explored terrains, and so expert experience and judgment are required (Zhang and Zhou 2015).…”

“…The integration of different evidential layers and the identification of favorable areas are key tasks in the MPM process (Zuo et al 2014;Zhang and Zhou 2015), and mathematical models play an important role in this procedure.In general, mathematical models for MPM can be categorized into knowledge-or data-driven constructs, Communicated by: H. A. Babaie although hybrid models also exist (Cheng and Wu 2017). In knowledge-driven techniques, few known mineral deposits are predicted in areas of interest; the approach is commonly used in poorly explored terrains, and so expert experience and judgment are required (Zhang and Zhou 2015). Knowledge-driven methods include constructs incorporating index overlay (Carranza et al 1999;Sadeghi and Khalajmasoumi 2015), the fuzzy analytical hierarchy process (Najafi et al 2014), fuzzy logic (Abedi et al 2013;Molan and Behnia 2013), wildcat mapping (Carranza 2010;Carranza and Hale 2002a), Dempster-Shafer belief theory or EBFs (e.g., Moon 1990;Hashemi Tangestani and Moore 2002;Abedi et al 2017),and the outranking method (e.g.,.…”

Synergy of Remote Sensing Data for Exploring Hydrothermal Mineral Resources Using GIS-Based Fuzzy Logic Approach

“…The remote-sensing approach affords significant tools for characterizing and delineating geological, structural, and lithological features that have helped identify areas of mineralization for many decades [3][4][5][6]. The substantial progress in processing remotely-sensed images has allowed for identifying rocks and minerals based on their spectral properties using multispectral and/or hyperspectral sensors in the visible-near-infrared (VNIR) and the shortwave infrared (SWIR) regions of the electromagnetic spectrum (EMS) [1][2][3][4][5][6][7][8][9][10][11][12][13]. Therefore, the use of remote-sensing has been extended to mineral exploration by careful characterization of fault/fracture zones and/or hydrothermal alteration minerals [1,8,9,[14][15][16][17] containing Al-OH, Fe-OH, Mg-OH, Si-OH, and -CO3 radicals [1,18,19].…”

“…The substantial progress in processing remotely-sensed images has allowed for identifying rocks and minerals based on their spectral properties using multispectral and/or hyperspectral sensors in the visible-near-infrared (VNIR) and the shortwave infrared (SWIR) regions of the electromagnetic spectrum (EMS) [1][2][3][4][5][6][7][8][9][10][11][12][13]. Therefore, the use of remote-sensing has been extended to mineral exploration by careful characterization of fault/fracture zones and/or hydrothermal alteration minerals [1,8,9,[14][15][16][17] containing Al-OH, Fe-OH, Mg-OH, Si-OH, and -CO3 radicals [1,18,19]. These key radicals are integral constituents of minerals that form by advanced argillic alteration (e.g., kaolinite and alunite) and phyllic alteration (e.g., sericite, illite), and they have recognized Al-OH absorption in the SWIR [15,[20][21][22] at certain wavelength regions, e.g., 2.205, 2.165, and 2.18 µm.…”