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Predictive modeling and comparative evaluation of geostatistical models for geochemical exploration through stream sediments
Arabian Journal of Geosciences Pub Date : 2020-10-10 , DOI: 10.1007/s12517-020-06062-7
Muhammad Ahsan Mahboob , Turgay Celik , Bekir Genc

Geochemical exploration of stream sediments is an important step for the identification of areas of interest with potential mineralization, particularly in the early stages of mineral exploration. A total of 407 sample points for 15 different geochemical traces were collected from Central Wales and classified into two groups: a training group consisting of 285 samples and a testing group consisting of 122 samples. Geospatial characterization of each parameter at stream level was performed using two different prediction models; the inverse distance weighting and the geostatistical kriging. Several variations of the IDW model was applied based on the power function and the number of the sample points, and the best one selected based on “root mean square prediction error” statistic. The same statistic was also used in the best-fitted semivariogram models including the circular, spherical, exponential and Gaussian for each geochemical parameter in Kriging. Finally, the mineral prospectivity map of the area was developed based on the geochemical accumulation index (GAI) using multivariate overlay analysis. The experimental results show that there is no single method that can be used independently to predict the spatial distribution of geochemical elements in streams. Instead, a combinatory approach of IDW and kriging is advised in order to generate more accurate predictions. The mineral prospectivity map based on GAI showed that most of the mineral-enriched streams were found in northwest, northeast, and south part of the study area which was also confirmed with the existing mining activities in the region.



中文翻译:

通过河流沉积物进行地球化学勘探的地统计学模型的预测建模和比较评估

河流沉积物的地球化学勘探是确定潜在矿化感兴趣区域的重要步骤,尤其是在矿物勘探的早期阶段。从威尔士中部收集了总共407个针对15种不同地球化学痕迹的采样点,并将其分为两组:训练组(包括285个样本)和测试组(包括122个样本)。使用两个不同的预测模型在流级别对每个参数进行地理空间表征;距离反比加权和地统计克里金法。根据幂函数和样本点数应用了IDW模型的几种变体,并根据“均方根预测误差”统计量选择了最佳变体。克里格(Kriging)中每个地球化学参数的最佳拟合半变异函数模型(包括圆形,球形,指数和高斯)也使用了相同的统计量。最后,利用多变量叠加分析,根据地球化学成藏指数(GAI),绘制了该地区的矿物远景图。实验结果表明,没有一种方法可以独立地用于预测河流中地球化学元素的空间分布。相反,建议使用IDW和克里金法的组合方法以生成更准确的预测。基于GAI的矿产远景图显示,大多数富矿流都位于研究区的西北,东北和南部,这也被该地区现有的采矿活动所证实。

更新日期:2020-10-11
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