The important goals of mineral exploration geochemistry are detection and identification of underlying mineralization. This paper deals with element concentration data analyzed of surface geochemical samples acquired from soil horizons or plants. A new unsupervised procedure is proposed for this purpose when the samples have been taken on a regular or irregular grid in the area under investigation. The methodology is based on Generalized Additive Model fits on element concentration data. Then new data points are taken of the surface of the smooth fits across the entire sampling area as a regular grid. Pairwise log-ratios of elements are then calculated of these points, and curvature of the log-ratio pairs is computed. High curvature indicates abrupt spatial changes, which could point at locations of mineralized zones. A measure called c-value evaluates the overall curvature and thus serves as an importance measure of a log-ratio pair. The methodology is tested on two real surface geochemical data sets collected in areas with known underlying mineralization, and the results confirm existing knowledge of the underlying mineralization.

矿物勘探地球化学的重要目标是探测和识别潜在的成矿作用。本文涉及分析从土壤层或植物中获取的地表地球化学样品的元素浓度数据。当样品在被调查区域的规则或不规则网格上采集时，为此目的提出了一种新的无监督程序。该方法基于对元素浓度数据进行拟合的广义加性模型。然后，将整个采样区域中平滑配合表面的新数据点作为规则网格。然后计算这些点的元素的成对对数比，并计算对数比对的曲率。高曲率表示突然的空间变化，这可能指向矿化带的位置。一项称为c值评估整体曲率，因此用作对数比对的重要指标。该方法论是在已知潜在成矿作用的地区收集的两个真实地表地球化学数据集上进行测试的，结果证实了有关潜在成矿作用的现有知识。