Geochemical mapping of risk element concentrations in soils is performed in countries around the world. It results in large datasets of high analytical quality, which can be used to identify soils that violate individual legislative limits for safe food production. However, there is a lack of advanced data mining tools that would be suitable for sensitive exploratory data analysis of big data while respecting the natural variability of soil composition. To distinguish anthropogenic contamination from natural variation, the analysis of the entire data distributions for smaller sub-areas is key. In this article, we propose a new data mining method for geochemical mapping data based on functional data analysis of probability density functions in the framework of Bayes spaces after post-stratification of a big dataset to smaller districts. Proposed tools allow us to analyse the entire distribution, going beyond a superficial detection of extreme concentration anomalies. We illustrate the proposed methodology on a dataset gathered according to the Czech national legislation (1990--2009). Taking into account specific properties of probability density functions and recent results for orthogonal decomposition of multivariate densities enabled us to reveal real contamination patterns that were so far only suspected in Czech agricultural soils. We process the above Czech soil composition dataset by first compartmentalising it into spatial units, in particular the districts, and by subsequently clustering these districts according to diagnostic features of their uni- and multivariate distributions at high concentration ends. Comparison between compartments is key to the reliable distinction of diffuse contamination. In this work, we used soil contamination by Cu-bearing pesticides as an example for empirical testing of the proposed data mining approach.

翻译：世界各国普遍开展土壤中风险元素浓度的地球化学填图工作，由此产生的高精度大样本数据集可用于识别违反安全食品生产立法限值的土壤。然而，目前缺乏既能进行大数据敏感探索性分析又能兼顾土壤组成自然变异性的先进数据挖掘工具。区分人为污染与自然变异的关键在于分析较小子区域内完整数据分布。本文提出一种基于贝叶斯空间框架下概率密度函数函数数据分析的地球化学填图数据挖掘新方法，该方法在将大数据集按区域后分层后，可分析完整分布特征，超越表层极端浓度异常检测。我们以捷克国家立法（1990-2009年）采集的数据集验证所提方法。通过考虑概率密度函数的特殊性质及多元密度正交分解的最新成果，本研究成功揭示了此前仅被怀疑存在的捷克农业土壤真实污染模式。处理上述捷克土壤组成数据集时，我们首先按空间单元（尤其是行政区）进行分区，进而根据各区高低浓度端单变量及多变量分布的诊断特征进行聚类。区域间比较是可靠区分弥散污染的关键。本研究以含铜农药导致的土壤污染为例，对所提数据挖掘方法进行实证检验。