Integrated GIS-based modelling for the quantitative prediction of magmatic Ti-V-Fe deposits: A case study in the Panzhihua-Xichang area of southwest China

Yuan Cong, Qingji Dong, Leon Bagas, Keyan Xiao, Kun Wang

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)

    Abstract

    The estimation of undiscovered mineral resources is an important part in the potential value of a mineral field and its long-term use. This manuscript documents six procedures for estimating the volume of potential mineral resources in an area that is based on integrated geological information forming part of a metallogenic model. The technique for this methodology includes: (1) the development a metallogenic model or models for mineral deposits in the study area; (2) the integration of the characteristics of known mineral anomalies with geology, geochemistry and geophysics to establish a predictive model for mineralization; (3) the definition of possible mineral resources incorporating predictive models with characteristic analyses, the knowledge-driven Delphi method, and magnetic anomalies; (4) calculate the parameters for the quantitative prediction, areas of known mineral deposits and their estimated dip extension, ore‐bearing coefficients, and their similarity coefficients based on explored ore-deposits in the area studied; (5) complete quantitative predictions using an synthetic volumetric equation; and (6) assess zones that are considered prospective for mineral resources. The procedure is tested for magmatic Ti-V-Fe resources in a known mineralized zone in the Sichuan Province of China.

    Original languageEnglish
    Pages (from-to)1102-1118
    Number of pages17
    JournalOre Geology Reviews
    Volume91
    DOIs
    Publication statusPublished - 1 Dec 2017

    Fingerprint

    Dive into the research topics of 'Integrated GIS-based modelling for the quantitative prediction of magmatic Ti-V-Fe deposits: A case study in the Panzhihua-Xichang area of southwest China'. Together they form a unique fingerprint.

    Cite this