Recognition and mapping of mineralization-related patterns in geochemical data is a key computational analysis to achieve a predictive model of prospectivity for mineral deposit occurrence. This contribution describes a spatio-geologically informed fuzzy classification (SGIFC) for portraying the spatial-frequency distribution of mineralization through integration of geochemical concentrations, 3D spatial properties, and geological knowledge contained in surface samples. A spatio-geological interaction model (SGIM) was defined to constitute an SGIFC by modulating fuzzy memberships in each iteration of standard fuzzy c-means (FCM) according to the incorporation of both spatial and geological inter-sample similarities. This strategy was adapted to the compositional nature of multi-elemental data and implemented by programming over packages supplied within the R language environment. A comparative experiment on soil samples collected in a porphyry Cu–Au system was subjected to understand how the SGIM affects standard memberships spatially, statistically, and geostatistically. Spatial autocorrelation analysis of fuzzy memberships through Moran’s I calculation and Monte Carlo simulation indicated that SGIFC leads to more robust spatially connected patterns compared to FCM models. Moreover, variogram analysis illustrated that SGIM orients the spatial continuity of mineralization-related memberships along the diffusion bearing of hydrothermal fluids across geological structures of the study area. Rock samples collected along trenches, as well as Cu volumetric productivities, were used as benchmarks to evaluate the integrity of the derived predictive models. The results reveal that the distribution pattern of mineralization-related memberships for SGIFC, compared to that of FCM, is more consistent with sub-outcropping and deep metallogenic realities and describes a more significant quantitative association with subsurface mineral content within the study region. The incorporation of SGIM into fuzzy classification has demonstrated the sufficiency to synthesize the comprehensive information of samples to predict effectively potential targets for follow-up exploration.

地球化学数据中与矿化有关的模式的识别和绘图是实现矿床成矿前景预测模型的关键计算分析。该文稿描述了一种时空地质信息模糊分类法（SGIFC），用于通过整合地球化学浓度，3D空间特性和地表样品中包含的地质知识来描绘矿化的空间频率分布。通过在标准模糊c的每次迭代中调制模糊隶属关系，定义了时空-地质相互作用模型（SGIM）来构成SGIFC-均值（FCM）是根据空间和地质样本间相似性的结合而得出的。该策略适应了多元素数据的组成性质，并通过对R语言环境中提供的程序包进行编程来实现。通过对斑岩型Cu-Au系统中收集的土壤样品进行的比较实验，了解了SGIM如何在空间，统计和地统计学上影响标准成员。通过Moran的I计算和蒙特卡洛模拟对模糊隶属度进行空间自相关分析，结果表明，与FCM模型相比，SGIFC导致了更鲁棒的空间连接模式。此外，变异函数图分析表明，SGIM沿着研究区域地质结构中沿热液扩散方向分布的矿化相关成员的空间连续性。沿沟收集的岩石样品以及铜的体积生产率均用作基准，以评估所推导的预测模型的完整性。结果表明，与FCM相比，SGIFC的矿化相关成员分布模式与地下露头和深部成矿现实更加一致，并且描述了与研究区域内地下矿物质含量的更重要的定量关联。