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Correlation between IP and Rs and grade data in modeling and evaluation of a copper deposit, case study: the Sarbisheh copper deposit, Iran

机译:IP和Rs与品位数据在铜矿床建模和评估中的相关性,案例研究:伊朗Sarbisheh铜矿床

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This paper addresses the application of integrated chargeability and resistivity method and grade data in modeling and evaluation of copper deposits. We argue that the relationship between IP, Rs and grade data may be used for modeling and reserve estimation and tested this argument for Sarbisheh copper deposit that is located in eastern Iran. Geology and mineralization situation of Sarbisheh deposit was reviewed. Then geophysical survey design was carried out based on the borehole exploration data and other parameters such as geological and topographical factors. Five profiles were designed and surveyed using dipole-dipole array. The obtained data was processed and 2D sections of IP and Rs were prepared for each profile by inverting the data using the Res2dinv software. Based on the geostatistical methods, a 3D block model for IP and Rs data was constructed using Datamine Studio software and this model was evaluated by some exploratory boreholes in the study area. The relationship between IP and Rs and copper grade has been calculated based on statistical and neural network methods. In the cases that borehole data was unavailable, Cu grade was estimated using regression and multivariate regression analysis. Moreover, Cu grade was predicted by neural network at unrecognized points. Then Cu grade was calculated for each block identified by IP 3D model. Finally, a 3D block model of this copper deposit was constructed. According to the drilling tests, there is a good correlation between 3D block model and real Cu grade modeling.
机译:本文阐述了综合的带电率和电阻率方法以及品位数据在铜矿床建模和评估中的应用。我们认为IP,Rs和品位数据之间的关系可用于建模和储量估算,并针对位于伊朗东部的Sarbisheh铜矿床测试了该论点。回顾了Sarbisheh矿床的地质和矿化情况。然后根据钻孔勘探数据和其他参数(例如地质和地形因素)进行地球物理勘测设计。使用偶极子-偶极子阵列设计和调查了五个剖面。通过使用Res2dinv软件反转数据,对获得的数据进行处理,并为每个配置文件准备IP和Rs的2D部分。基于地统计方法,使用Datamine Studio软件构建了IP和Rs数据的3D块模型,并通过研究区域的一些勘探井对了该模型进行了评估。 IP和Rs与铜品位之间的关系已基于统计和神经网络方法进行了计算。在无法获得井眼数据的情况下,可使用回归和多元回归分析估算铜品位。而且,铜品位是通过神经网络在无法识别的点进行预测的。然后针对IP 3D模型确定的每个块计算Cu品位。最后,构建了该铜矿床的3D块模型。根据钻探测试,在3D块模型和真实Cu品位模型之间存在良好的相关性。

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