通过科学共识进行矿产勘探:南非 PGE-Ni-Cu-Cr 和 Witwatersrand 型金矿床的首批国家远景图

IF 4.8 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Glen T. Nwaila, Steven E. Zhang, Julie E. Bourdeau, Emmanuel John M. Carranza, Stephanie Enslin, Musa S. D. Manzi, Fenitra Andriampenomanana, Yousef Ghorbani
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引用次数: 0

摘要

我们在此介绍第一种基于实验科学(共识)的矿产远景规划(MPM)方法及其验证结果,即南非 PGE-Ni-Cu-Cr 和 Witwatersrand 型金矿床的国家远景规划图和数据集。研究目标是(1) 开发应用性方法;(2) 尽可能验证该方法的有效性;(3) 提供国家 MPM 产品。通过对世界上最大、开采最好的地质系统和矿区--布什维尔德复合区和威特沃特斯兰德盆地--内的特大型矿床进行研究,验证了多金属结核方法。它们无与伦比的知识和超大型矿床的地位使其成为 "认证参考目标",对验证 MPM 方法最为有用。我们的 MPM 方法是通过深度集合构建科学共识,利用工作流程实验,通过模仿数据科学家的集合结果来传播主观工作流程选择的不确定性。共识模型相当于专家汇总的数据驱动模型,从而增强了对我们的 MPM 产品的信心。通过捕捉工作流程引起的不确定性,该研究产生的 MPM 产品不仅突出了潜在的勘探目标,还为每个目标提供了空间共识水平,从而降低了下游勘探的风险。我们的 MPM 结果在质量上与勘探和地质知识一致。特别是,我们的方法确定了已知勘探区域内的高勘探前景区域,并且在地质和地球空间上与两个矿物系统的已知范围相对应。集合的收敛率表明,我们的 MPM 产品具有很高的统计持久性,这表明它们可以指导全国范围内的勘探工作,直到出现重要的新数据。对于 PGE-Ni-Cu-Cr 而言,潜在的新勘探目标位于布什维尔德复合体西北部;对于 Au 而言,有希望的区域位于威特沃特斯兰德盆地西部。这项工作对矿产行业具有深远的影响。随着勘探变得越来越以数据为驱动,必须解决对 MPM 产品的信任问题;可以使用所提出的科学方法来解决这个问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa

Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa

We present here the first experimental science (consensus)-based mineral prospectivity mapping (MPM) method and its validation results in the form of national prospectivity maps and datasets for PGE–Ni–Cu–Cr and Witwatersrand-type Au deposits in South Africa. The research objectives were: (1) to develop the method toward applicative uses; (2) to the extent possible, validate the effectiveness of the method; and (3) to provide national MPM products. The MPM method was validated by targeting mega-deposits within the world’s largest and best exploited geological systems and mining districts—the Bushveld Complex and the Witwatersrand Basin. Their incomparable knowledge and mega-deposit status make them the most useful for validating MPM methods, serving as “certified reference targets”. Our MPM method is built using scientific consensus via deep ensemble construction, using workflow experimentation that propagates uncertainty of subjective workflow choices by mimicking the outcome of an ensemble of data scientists. The consensus models are a data-driven equivalent to expert aggregation, increasing confidence in our MPM products. By capturing workflow-induced uncertainty, the study produced MPM products that not only highlight potential exploration targets but also offer a spatial consensus level for each, de-risking downstream exploration. Our MPM results agree qualitatively with exploration and geological knowledge. In particular, our method identified areas of high prospectivity in known exploration regions and geologically and geospatially corresponding to the known extents of both mineral systems. The convergence rate of the ensemble demonstrated a high level of statistical durability of our MPM products, suggesting that they can guide exploration at a national scale until significant new data emerge. Potential new exploration targets for PGE–Ni–Cu–Cr are located northwest of the Bushveld Complex; for Au, promising areas are west of the Witwatersrand Basin. The broader implications of this work for the mineral industry are profound. As exploration becomes more data-driven, the question of trust in MPM products must be addressed; it can be done using the proposed scientific method.

Graphical Abstract

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来源期刊
Natural Resources Research
Natural Resources Research Environmental Science-General Environmental Science
CiteScore
11.90
自引率
11.10%
发文量
151
期刊介绍: This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.
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