利用地球物理数据集上的 "SWARA "模型绘制尼日利亚西南部伊莱萨片岩带部分地区的金矿远景图

Ayokunle Adewale Akinlalu, Oluwapelumi Idowu Obideyi, Daniel Oluwafunmilade Afolabi , Kola Abdul-Nafiu Adiat, Oluwamayowa Joseph Adeola
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摘要

本研究采用了一个知识驱动模型(逐步权重评估比率分析(SWARA)),用于绘制尼日利亚西南部伊莱萨片岩带部分地区的金矿远景图。本研究使用了航磁、航空辐射测量和遥感(大气空间热发射和反射辐射计(ASTER)数据)数据集。对航磁数据进行了数据增强技术处理,以绘制研究区域的线状图。此外,还利用对空气辐射计和 ASTER 数据集的分析来划定研究区域的热液蚀变区。考虑了岩性、线状密度、热液蚀变和坡度等因素,利用 SWARA 模型进行权重分配,绘制了研究区域的金矿潜力图。研究表明,东北-西南走向的构造有助于热液和成矿流体在研究区域的输送。此外,还观察到金矿化主要发生在花岗岩上。SWARA 模型的权重分配结果表明,影响金矿化的因素依次为热液蚀变、岩性、线状密度和坡度。根据权重分配绘制的金矿化地图将研究区域划分为五个等级:背景、低、中、高和极高,研究区域的南部、西部和西北部轴线具有中到极高的金矿化潜力。利用矿坑进行定性验证的成功率为 73%,而利用线性回归模型进行定量验证的成功率为 74%,这证明了模型的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gold prospectivity mapping using “SWARA” model on geophysical datasets in parts of Ilesa Schist belt, Southwestern Nigeria
This study employed a knowledge–driven model (stepwise weight assessment ratio analysis (SWARA)) for the prospectivity mapping of gold in parts of Ilesa Schist belt, Southwestern Nigeria. Aeromagnetic, aero-radiometric and remote sensing (Atmospheric Spaceborne Thermal Emission and reflection Radiometer (ASTER) data) datasets were utilized for this study. Data enhancement techniques were performed on the aeromagnetic data to produce the lineament map of the study area. Also, analyses of the aeroradiometric and ASTER datasets were used in delineating hydrothermally altered zones in the study area. Lithology, lineament density, hydrothermal alteration and slope were the factors considered to produce the gold potential map of the study area using the SWARA model for weight assignment. The study showed that NE–SW trending structures aid the transportation of hydrothermal and mineralizing fluids in the study area. Furthermore, gold mineralisation is observed to occur majorly on the granitoids. From the SWARA model, weight assignment results showed that the factors influencing gold mineralisation in descending order of importance are hydrothermal alteration, lithology, lineament density and slope. The produced gold mineralisation map based on the weight assignment classified the study area into five classes: background, low, moderate, high and very high with the southern, western and northwestern axis of the study area having moderate to very high potential of gold mineralisation. Qualitative validation using mining pits showed 73 % success rate while quantitative validation utilizing linear regression model showed a success rate of 74 % substantiating the reliability of the model.
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