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：世界各地国家均开展土壤风险元素浓度的地球化学填图工作，由此产生的高分析质量大数据集可用于识别违反食品安全生产立法限值的土壤。然而，目前仍缺乏既能尊重土壤成分自然变异性，又适用于大数据敏感探索性数据分析的先进数据挖掘工具。为区分人为污染与自然变异，关键需分析较小子区域的数据整体分布。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新挖掘方法，该方法在对大数据集进行后分层处理形成较小行政区划后，可分析数据整体分布，超越对极端浓度异常的浅层检测。我们以捷克国家立法（1990-2009年）采集的数据集为例说明所提出的方法体系。通过考虑概率密度函数的特定属性及多元密度正交分解的最新成果，我们得以揭示此前仅在捷克农业土壤中疑似存在的真实污染模式。我们首先将上述捷克土壤成分数据集按空间单元（特别是行政区）进行分区，继而根据各区高浓度端单变量和多变量分布的诊断特征进行聚类分析。区间比较是可靠区分扩散污染的关键。本研究以含铜农药导致的土壤污染为例，对所提出的数据挖掘方法进行实证检验。