Lisheen Mine in County Tipperary, Ireland exploited an underground Pb/Zn massive sulphide deposit hosted in Carboniferous (Mississippian) carbonates. During the extraction phase, the mine workings (located at an average depth of 170 m below ground level), were continuously pumped to lower the groundwater level. Following mine closure in 2015, pumping ceased and eight groundwater wells in the surrounding area were sampled monthly over an 11-month period to monitor the effects of groundwater rebound. These wells draw water from the upper 30 m of a limestone/dolostone aquifer and the monthly samples were analysed for the concentration of 31 elements and compounds (SO₄, Cl, NO₃, F, NH₄, NO₂, P, Ca, Na, K, Mg, Fe, Mn, Cu, Zn, Pb, Al, Ni, Ba, As, Hg, B, Cr, Cd, Mo, Ag, Co, Sr, Be, Sb and U). All of the water can be described as Ca–HCO₃ type as expected. Standard methods for analysing groundwater geochemistry data (e.g. piper diagrams etc.) are useful, differentiating groundwaters with first-order contrasting chemical signatures, for example, distinguishing Ca–HCO₃-type from Na–HCO₃-type water. Samples from the 8 monitoring wells appear to be broadly similar, using this approach. However, these major ion methods fail to further distinguish between different groundwaters. The use of multivariate statistical analytical techniques has become more common in groundwater studies in recent years, allowing the interaction of all the elements and compounds to be considered simultaneously. Compositional Data Analysis (CoDA) was used on the Lisheen dataset to gain a better understanding of the spatial and temporal variation in groundwater geochemistry. Ilr-ion plotting, Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) through CoDA highlights the elements and compounds that account for the majority of the variance and at Lisheen these are nitrate, manganese, ammonium, sulphate and potassium. By displaying these data visually in a CoDA bi-plot, each location can be reliably ‘geochemically fingerprinted’ despite similar concentrations of major ions within a relatively small geographical area (<30 km²). Relabeling the bi-plot observations by date of recovery reveals how one particular groundwater well (PH) subtly varies over time, most likely as a result of seasonal land-use changes (input of compounds associated with fertiliser). This type of statistical analysis has broad applications in hydrology and hydrogeology including contaminant tracing and interaction, environmental studies, land-use planning and mineral exploration.

爱尔兰蒂珀雷里郡的利希恩矿山开采了地下含铅/锌块状硫化物矿床，该矿床含在石炭系（密西西比州）碳酸盐中。在开采阶段，连续抽运矿山作业（位于地面平均深度以下170 m）以降低地下水位。2015年矿山关闭后，停止抽水，并在11个月内每月对周边地区的8口地下水井进行采样，以监测地下水回弹的影响。这些井从石灰石/白云岩含水层的上部30 m吸水，每月分析样品中31种元素和化合物（SO ₄，Cl，NO ₃，F，NH ₄，NO _2）的浓度。，P，Ca，Na，K，Mg，Fe，Mn，Cu，Zn，Pb，Al，Ni，Ba，As，Hg，B，Cr，Cd，Mo，Ag，Co，Sr，Be，Sb和U ）。如所期望的，所有的水都可以描述为Ca–HCO ₃类型。分析地下水地球化学数据的标准方法（例如吹管图等）非常有用，可以区分具有一阶对比化学特征的地下水，例如，区分Ca–HCO ₃型和Na–HCO ₃型水。使用这种方法，来自8个监测井的样品看起来大致相似。但是，这些主要的离子方法无法进一步区分不同的地下水。近年来，在地下水研究中使用多元统计分析技术变得更加普遍，可以同时考虑所有元素和化合物的相互作用。在Lisheen数据集上使用了成分数据分析（CoDA），可以更好地了解地下水地球化学的时空变化。通过CoDA进行的离子图绘制，层次聚类分析（HCA）和主成分分析（PCA）突出显示了占大部分差异的元素和化合物，在Lisheen，它们是硝酸盐，锰，铵，硫酸盐和钾。²）。通过恢复日期重新标记双图观测值，可以揭示一个特定的地下水井（PH）如何随时间微妙地变化，这很可能是由于季节性土地利用变化（与肥料相关的化合物的输入）而造成的。这种类型的统计分析在水文和水文地质学中具有广泛的应用，包括污染物追踪和相互作用，环境研究，土地利用规划和矿物勘探。