Gaussian processes (GPs) provide statistically optimal predictions in the sense of unbiasedness and maximal precision. Although the modern implementation of GPs as a machine learning technique is more capable and flexible than Kriging, their employment in environmental science is less routine. Their flexibility and capability as a spatial data interpolation technique are demonstrated by applying them to groundwater salinity prediction in a data-sparse region in Australia. By learning from multiple data sources, including AEM and DEM data, GPs have generated groundwater salinity maps with rich local details and quantified uncertainty to support risk-based decision making. The results demonstrate the great worth of nonpoint data with regional spatial coverage to provide more realistic heterogeneity in aquifer properties that are critical for many studies such as contaminant transport. GPs should be further encouraged in groundwater science for data interpolation and prediction, especially when point measurements are sparse and multiple predictors are available.

高斯过程 (GP) 在无偏性和最大精度的意义上提供了统计上的最佳预测。尽管 GP 作为机器学习技术的现代实施比克里金法更有能力和灵活性，但它们在环境科学中的应用却不太常规。通过将它们应用于澳大利亚数据稀疏地区的地下水盐度预测，证明了它们作为空间数据插值技术的灵活性和能力。通过从包括 AEM 和 DEM 数据在内的多个数据源中学习，GP 生成了具有丰富局部细节和量化不确定性的地下水盐度图，以支持基于风险的决策。结果证明了具有区域空间覆盖的非点数据的巨大价值，可以为含水层特性提供更​​现实的异质性，这对许多研究（如污染物迁移）至关重要。应进一步鼓励全球定位系统在地下水科学中进行数据插值和预测，尤其是在点测量稀疏且有多个预测因子可用的情况下。