Mineral potential modeling of porphyry copper deposits using continuously-weighted spatial evidence layers and union score integration method

IF 1.1 Q3 MINING & MINERAL PROCESSING Journal of Mining and Environment Pub Date : 2021-04-21 DOI:10.22044/JME.2021.10668.2026
Esmaeil Bahri, A. Alimoradi, M. Yousefi
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引用次数: 1

Abstract

There are different exploration methods, each of which may introduce a number of promising exploration targets. However, due to the financial and time constraints, only a few of them are selected as the exploration priorities. Instead of the individual use of any exploration method, it is common to integrate the results of different methods in an interdependent framework in order to recognize the best targets for further exploration programs. In this work, the continuously-weighted evidence maps of proximity to intrusive contacts, faults density, and stream sediment geochemical anomalies of a set of porphyry copper deposits in the Jiroft region of the Kerman Province in Iran are first generated using the logistic functions. The weighted evidence maps are then integrated using the union score integration function in order to model the deposit type in the studied area. The weighting and integration approaches applied avoid the disadvantages of the traditional methods in terms of carrying the bias and error resulting from the weighting procedure. Evaluation of the ensuing prospectivity model generated demonstrate that the prediction rate of the model is acceptable, and the targets generated are reliable to follow up the exploration program in the studied area.
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基于连续加权空间证据层和联合积分法的斑岩铜矿成矿潜力建模
有不同的勘探方法,每种方法都可能引入一些有希望的勘探目标。然而,由于资金和时间的限制,只有少数几个被选为勘探重点。为了确定进一步勘探计划的最佳目标,通常将不同方法的结果整合在一个相互依赖的框架中,而不是单独使用任何勘探方法。在这项工作中,首次使用logistic函数生成了伊朗Kerman省Jiroft地区一组斑岩铜矿床的侵入接触邻近度、断层密度和水系沉积物地球化学异常的连续加权证据图。然后利用联合分数积分函数对加权证据图进行综合,从而对研究区沉积类型进行建模。所采用的加权和积分方法避免了传统方法由于加权过程所带来的偏差和误差的缺点。对所建立的远景模型进行了评价,结果表明该模型的预测率是可以接受的,所生成的靶区对研究区后续勘探方案是可靠的。
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来源期刊
Journal of Mining and Environment
Journal of Mining and Environment MINING & MINERAL PROCESSING-
CiteScore
1.90
自引率
25.00%
发文量
0
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