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Evolutionary analysis of groundwater flow: Application of multivariate statistical analysis to hydrochemical data in the Densu Basin, Ghana

机译:地下水流的演化分析:多元统计分析在加纳登苏盆地水化学数据中的应用

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摘要

An evolutionary trend has been postulated through the analysis of hydrochemical data of a crystalline rock aquifer system in the Densu Basin, Southern Ghana. Hydrochemcial data from 63 groundwater samples, taken from two main groundwater outlets (Boreholes and hand dug wells) were used to postulate an evolutionary theory for the basin. Sequential factor and hierarchical cluster analysis were used to disintegrate the data into three factors and five clusters (spatial associations). These were used to characterize the controls on groundwater hydrochemistry and its evolution in the terrain. The dissolution of soluble salts and cation exchange processes are the dominant processes controlling groundwater hydrochemistry in the terrain. The trend of evolution of this set of processes follows the pattern of groundwater flow predicted by a calibrated transient groundwater model in the area. The data suggest that anthropogenic activities represent the second most important process in the hydrochemistry. Silicate mineral weathering is the third most important set of processes. Groundwater associations resulting from Q-mode hierarchical cluster analysis indicate an evolutionary pattern consistent with the general groundwater flow pattern in the basin. These key findings are at variance with results of previous investigations and indicate that when carefully done, groundwater hydrochemical data can be very useful for conceptualizing groundwater flow in basins. (C) 2017 Elsevier Ltd. All rights reserved.
机译:通过分析加纳南部登苏盆地的结晶岩含水层系统的水化学数据,可以推测出其演化趋势。从两个主要地下水出口(钻孔和手挖井)获得的63个地下水样品的水化学数据被用来推测该盆地的演化理论。使用顺序因素和层次聚类分析将数据分解为三个因素和五个聚类(空间关联)。这些被用来表征对地下水水化学及其在地形中演变的控制。可溶性盐的溶解和阳离子交换过程是控制地形中地下水水化学的主要过程。这套过程的演变趋势遵循该地区校准的瞬态地下水模型预测的地下水流模式。数据表明,人为活动是水化学中第二重要的过程。硅酸盐矿物风化是第三组最重要的过程。由Q型层次聚类分析得出的地下水协会表明,演化模式与盆地中一般的地下水流模式一致。这些关键发现与以前的调查结果不一致,表明如果仔细进行研究,地下水水化学数据对于概念化盆地中的地下水流量非常有用。 (C)2017 Elsevier Ltd.保留所有权利。

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