This paper presents a new interaction point process that integrates geological knowledge for the purpose of automatic sources detection of multiple sources in groundwaters from hydrochemical data. The observations are considered as spatial data, that is a point cloud in a multi-dimensional space of hydrogeochemical parameters. The key hypothesis of this approach is to assume the unknown sources to be the realisation of a point process. The probability density describing the sources distribution is built in order to take into account the multi-dimensional character of the data and specific physical rules. These rules induce a source configuration able to explain the observations. This distribution is completed with prior knowledge regarding the model parameters distributions. The composition of the sources is estimated by the configuration maximising the joint proposed probability density. The method was first calibrated on synthetic data and then tested on real data from hydrothermal systems.

中文翻译：

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HUG 模型：从水化学数据中贝叶斯检测地下水中多个来源的交互点过程

本文提出了一种新的相互作用点过程，该过程整合了地质知识，用于从水化学数据中自动检测地下水中的多个来源。观测被视为空间数据，即水文地球化学参数多维空间中的点云。这种方法的关键假设是假设未知来源是一个点过程的实现。建立描述源分布的概率密度是为了考虑数据的多维特征和特定的物理规则。这些规则引入了能够解释观察结果的源配置。该分布是在有关模型参数分布的先验知识的情况下完成的。通过最大化联合提议的概率密度的配置来估计源的组成。该方法首先在合成数据上进行校准，然后在热液系统的真实数据上进行测试。