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Gold Prospectivity Mapping in the Sonakhan Greenstone Belt, Central India: A Knowledge-Driven Guide for Target Delineation in a Region of Low Exploration Maturity
Natural Resources Research ( IF 5.4 ) Pub Date : 2021-10-18 , DOI: 10.1007/s11053-021-09962-x
Satyabrata Behera 1 , Mruganka K. Panigrahi 1
Affiliation  

The Sonakhan greenstone belt in Central India is under-explored with respect to gold in spite of its similarity to auriferous greenstone belts in general, which prompted a prospectivity analysis. The workflow involved adoption of a conceptual mineral systems model, recognizing indicative spatial proxies, processing exploration datasets, generating evidence maps and integrating into GIS-based mineral prospectivity mapping. Available geological information such as key lithologic units and their contacts was combined with geochemical anomalies of selected pathfinder elements, geophysical data (aeromagnetic anomaly and K/Th ratio map) and satellite digital image data (ASTER and Landsat 7 ETM +), leading to generation of 17 evidential layers. The lack of a significant number of known mineral occurrences in the study area precludes the use of data-driven prospectivity modeling techniques. Therefore, knowledge-driven approaches such as binary and multiclass index overlay, fuzzy logic and fuzzy AHP (analytic hierarchy process) were adopted to integrate the evidential layers resulting in four prospectivity maps. The variation of cumulative prospectivity with respect to cumulative area in each model was used to determine threshold to produce binary prospectivity maps separating high and low prospectivity zones. An approach based on the unique conditions of the binary prospectivity maps was used to illustrate the combined results of different models. In order to quantify the intuitive uncertainty in exploration targeting that arose due to different model outputs, a modulated predictive model was generated taking the mean prospectivity values at each pixel. The pixels having mean values above 95th percentile were grouped and the area delineated as potential exploration targets for gold that comprises merely 5% of the study area. The estimated uncertainty and confidence values for each pixel were used in the risk analysis that returned 1.95% and 3.05% of the study area as low- and high-risk exploration targets, respectively.



中文翻译:

印度中部 Sonakhan 绿岩带的金矿远景图:低勘探成熟度区域目标划定的知识驱动指南

尽管总体上与含金绿岩带相似,但印度中部的 Sonakhan 绿岩带对黄金的勘探不足,这促使进行了前景分析。工作流程包括采用概念性矿物系统模型、识别指示性空间代理、处理勘探数据集、生成证据图并集成到基于 GIS 的矿物勘探绘图中。关键岩性单元及其接触面等可用地质信息与选定探路者元素的地球化学异常、地球物理数据(航磁异常和 K/Th 比图)和卫星数字图像数据(ASTER 和 Landsat 7 ETM +)相结合,导致生成17 个证据层。研究区内缺乏大量已知矿物,因此无法使用数据驱动的前景建模技术。因此,采用二元和多类索引叠加、模糊逻辑和模糊AHP(层次分析法)等知识驱动方法来整合证据层,形成四个前景图。每个模型中累积前景相对于累积面积的变化被用来确定阈值,以生成分隔高低前景区的二元前景图。一种基于二元远景图独特条件的方法被用来说明不同模型的组合结果。为了量化由于不同模型输出而引起的勘探目标的直观不确定性,使用每个像素的平均前景值生成调制预测模型。平均值高于第 95 个百分位数的像素被分组,该区域被划定为黄金的潜在勘探目标,仅占研究区域的 5%。每个像素的估计不确定性和置信度值用于风险分析,分别返回 1.95% 和 3.05% 的研究区域作为低风险和高风险勘探目标。

更新日期:2021-10-19
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