利用神经网络从岩石物性数据估计矿山矿石品位

Gaurav Nagpal, Singh Shrikant Ramesh, Naga Vamsi Krishna Jasti, Ankita Nagpal, G. Sharma
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引用次数: 0

摘要

矿石的品位在采矿工业中起着非常重要的作用。根据岩石物性资料,可以较准确地预测矿石品位。然而,现有文献对数据分析技术在数据帮助下进行矿石品位估算的研究却闭口不谈。利用井内物探采集的矿石岩石物性数据,捕获了该矿的21个性质,利用多层神经网络感知器模型和神经网络回归模型进行品位预测,能够较准确地估算出该矿的品位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ore Grade Estimation in Mining Industry from petro-physical data using neural networks
The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data. The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
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