Fuzzy outranking approach: A knowledge-driven method for mineral prospectivity mapping

被引:59
|
作者
Abedi, Maysam [1 ]
Norouzi, Gholam-Hossain [1 ]
Fathianpour, Nader [2 ]
机构
[1] Univ Tehran, Dept Min Engn, Coll Engn, Tehran, Iran
[2] Isfahan Univ Technol, Dept Min Engn, Esfahan, Iran
关键词
Knowledge-driven method; Mineral prospectivity mapping; Various geo-data sets; Fuzzy Outranking Method; ASTER Data; Porphyry Deposit; 2-DIMENSIONAL MAGNETIC BODIES; NORTHERN FENNOSCANDIAN SHIELD; OROGENIC GOLD; ANALYTIC SIGNAL; ALTERED ROCKS; ASTER DATA; DEPOSITS; EXPLORATION; INTEGRATION; LOGIC;
D O I
10.1016/j.jag.2012.07.012
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
This paper describes the application of a new multi-criteria decision making (MCDM) technique called fuzzy outranking to map prospectivity for porphyry Cu-Mo deposits. Various raster-based evidential layers involving geological, geophysical, and geochemical geo-data sets are integrated for mineral prospectivity mapping (MPM). In a case study, 13 layers of the Now Chun deposit located in the Kerman province of Iran are used to explore the region of interest. The outputs are validated using 21 boreholes drilled in this area. Comparison of the output prospectivity map with concentrations of Cu and Mo in the boreholes indicates that the fuzzy outranking MCDM is a useful tool for MPM. The proposed method shows a high performance for MPM thereby reducing the cost of exploratory drilling in the study area. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:556 / 567
页数:12
相关论文
共 50 条
  • [31] Knowledge-Driven Prospectivity Mapping for Granite-Related Polymetallic Sn–F–(REE) mineralization, Bushveld Igneous Complex, South Africa
    Litshedzani Mutele
    Alazar Billay
    John Paul Hunt
    Natural Resources Research, 2017, 26 : 535 - 552
  • [32] Optimizing a Knowledge-driven Prospectivity Model for Gold Deposits Within Perapohja Belt, Northern Finland
    Nykanen, V.
    Niiranen, T.
    Molnar, F.
    Lahti, I.
    Korhonen, K.
    Cook, N.
    Skytta, P.
    NATURAL RESOURCES RESEARCH, 2017, 26 (04) : 571 - 584
  • [33] Gold Prospectivity Mapping in the Sonakhan Greenstone Belt, Central India: A Knowledge-Driven Guide for Target Delineation in a Region of Low Exploration Maturity
    Satyabrata Behera
    Mruganka K. Panigrahi
    Natural Resources Research, 2021, 30 : 4009 - 4045
  • [34] Artificial neural networks: a new method for mineral prospectivity mapping
    Brown, WM
    Gedeon, TD
    Groves, DI
    Barnes, RG
    AUSTRALIAN JOURNAL OF EARTH SCIENCES, 2000, 47 (04) : 757 - 770
  • [35] Gold Prospectivity Mapping in the Sonakhan Greenstone Belt, Central India: A Knowledge-Driven Guide for Target Delineation in a Region of Low Exploration Maturity
    Behera, Satyabrata
    Panigrahi, Mruganka K.
    NATURAL RESOURCES RESEARCH, 2021, 30 (06) : 4009 - 4045
  • [36] Cobalt Prospectivity Using a Conceptual Fuzzy Logic Overlay Method Enhanced with the Mineral Systems Approach
    Nykanen, Vesa
    Tormanen, Tuomo
    Niiranen, Tero
    NATURAL RESOURCES RESEARCH, 2023, 32 (06) : 2387 - 2416
  • [37] Fuzzy clustering with pairwise constraints for knowledge-driven image categorisation
    Grira, N.
    Crucianu, M.
    Boujemaa, N.
    IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 2006, 153 (03): : 299 - 304
  • [38] Cobalt Prospectivity Using a Conceptual Fuzzy Logic Overlay Method Enhanced with the Mineral Systems Approach
    Vesa Nykänen
    Tuomo Törmänen
    Tero Niiranen
    Natural Resources Research, 2023, 32 : 2387 - 2416
  • [39] Attention-driven graph convolutional neural networks for mineral prospectivity mapping☆
    Cao, Changjie
    Wang, Xiuliang
    Yang, Fan
    Xie, Miao
    Liu, Bingli
    Kong, Yunhui
    Li, Cheng
    Zhou, Zhongli
    ORE GEOLOGY REVIEWS, 2025, 180
  • [40] Fuzzy-Cuts: A Knowledge-Driven Graph-Based Method for Medical Image Segmentation
    Chittajallu, D. R.
    Brunner, G.
    Kurkure, U.
    Yalamanchili, R. P.
    Kakadiaris, I. A.
    CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4, 2009, : 715 - +