结合地球物理属性与布谷鸟搜索机器学习新算法估算银品位——以扎寿然金矿为例

IF 1.1 Q3 MINING & MINERAL PROCESSING
A. Alimoradi, B. Maleki, A. Karimi, M. Sahafzadeh, S. Abbasi
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引用次数: 1

摘要

勘探方法分为直接勘探和间接勘探两大类。其中,间接物探法比直接物探法更省时、更经济。地球物理调查的目标是从地下地物中获得准确的图像。感应极化法是金属硫化物矿探测的常用方法之一。由于扎寿冉矿区金属矿在寄主岩中较为分散,电激电法是主要的找矿方法。与激电法并行,进行电阻率数据采集和处理,以获得更准确的解释。在这项工作中,我们尝试使用布谷鸟搜索机器学习算法将IP/RS地球物理属性与钻孔品位分析和地质信息相结合,以估计银品位值。结果表明,利用地球物理资料准确估计品位值是可行的,特别是在没有钻探资料的地区。这减少了勘探和估计储量的成本和时间。将智能反演结果与数值反演结果进行对比,发现智能反演结果与数值反演结果具有较好的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Geophysical Attributes with New Cuckoo Search Machine-Learning Algorithm to Estimate Silver Grade Values–Case Study: Zarshouran Gold Mine
The exploration methods are divided into the direct and indirect categories. Among these, the indirect geophysical methods are more time- and cost-effective compared with the direct methods. The target of the geophysical investigations is to obtain an accurate image from the underground features. The Induced polarization (IP) is one of the common methods used for metal sulfide ore detection. Since metal ores are scattered in the host rock in the Zarshouran mine area, IP is considered as a major exploration method. Parallel to IP, the resistivity data gathering and processing are done to get a more accurate interpretation. In this work, we try to integrate the IP/RS geophysical attributes with borehole grade analyses and geological information using the cuckoo search machine-learning algorithm in order to estimate the silver grade values. The results obtained show that it is possible to estimate the grade values from the geophysical data accurately, especially in the areas without drilling data. This reduces the costs and time of the exploration and ore reserves estimation. Comparing the results of the intelligent inversion with the numerical methods, as the major tools to invert the geophysical data to the ore model, demonstrate a superior correlation between the results.
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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%
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