Abstract This research demonstrates the utility of groundwater hydrochemical data for facilitating lithological mapping in a sedimentary terrain. Factor analysis and Q-mode Hierarchical Cluster Analysis (HCA) were applied to groundwater hydrochemical data from the Nasia Basin in Northern Ghana to determine the major sources of hydrochemical variation and the main spatial groundwater associations (groups). These analyses were complemented by mass balance hydrochemical modeling from PHREEQC and petrographical analysis of rock samples from the area. Rock samples from the study area were subjected to petrographical and geochemical analyses from a petrographic microscope and Scanning Electron Microscope (SEM) respectively. Four principal factors were distinguished from the factor analysis, highlighting four principal processes responsible for the observed hydrochemistry in the area. These four factors cumulatively account for over 79% of the total variance in the dataset. Factor scores were computed from the factor loadings matrix through a regression process, to highlight the spatial variations in the significance of each of the factors in the terrain. On the basis of the parameter loadings, the first factor, which has been noted to be pervasive throughout the terrain, has been interpreted to represent silicate mineral weathering. Both the geochemical data and petrographical analysis indicate the presence of silicate minerals indicating the plausibility of silicate mineral weathering. The process of silicate mineral weathering appears to be highly pervasive throughout the basin, indicating high factor scores in places. The trend of spatial variations in the scores of factor 1 clearly mimics the known geology of the terrain, indicating that the geology is the main controlling factor in the hydrochemistry of groundwater in the basin. This pattern, exhibited by the factor scores indicates that where adequate groundwater hydrochemical data is available, factor analysis would prove useful in mapping out lithological variations in an area. Two of the remaining factors have been observed to represent other specific geogenic processes. Mass balance hydrochemical modeling suggests that albite may be in active dissolution, presenting pervasively negative saturation indices (undersaturation), while kaolinite is extensively supersaturated. Five groundwater associations distinguished from the Q-mode HCA display a spatial pattern which identifies with the underlying geology.

中文翻译：

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Voltaian 超群的 Panabako、Kodjari 和 Bimbilla 地层的地质和水文地质框架——来自地下水水化学数据的启示

摘要 这项研究证明了地下水水化学数据在沉积地形中促进岩性绘图的效用。因子分析和 Q 模式分层聚类分析 (HCA) 被应用于来自加纳北部 Nasia 盆地的地下水水化学数据，以确定水化学变化的主要来源和主要的空间地下水关联（组）。这些分析得到了来自 PHREEQC 的质量平衡水化学建模和该地区岩石样品的岩相分析的补充。研究区的岩石样品分别用岩相显微镜和扫描电子显微镜 (SEM) 进行了岩相和地球化学分析。从因子分析中区分出四个主要因素，强调负责该地区观测到的水化学的四个主要过程。这四个因素累计占数据集中总方差的 79% 以上。通过回归过程从因子载荷矩阵计算因子得分，以突出地形中每个因子显着性的空间变化。在参数载荷的基础上，第一个已被注意到在整个地形中普遍存在的因素已被解释为代表硅酸盐矿物风化。地球化学数据和岩相分析都表明存在硅酸盐矿物，表明硅酸盐矿物风化的可能性。整个盆地的硅酸盐矿物风化过程似乎非常普遍，表明某些地方的因子得分很高。因子 1 得分的空间变化趋势明显模拟了已知的地形地质，表明地质是盆地地下水水化学的主要控制因素。因子得分所显示的这种模式表明，在可以获得足够的地下水水化学数据的情况下，因子分析将被证明有助于绘制一个区域的岩性变化。已观察到其余两个因素代表其他特定的地质过程。质量平衡水化学模型表明钠长石可能处于活跃溶解状态，呈现出普遍的负饱和指数（不饱和），而高岭石则广泛地过饱和。与 Q 模式 HCA 不同的五个地下水关联显示出与底层地质相关的空间模式。