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.

翻译：本文提出了一种融合地质知识的交互点过程，用于从水化学数据中自动检测地下水的多来源。观测数据被视为空间数据，即水地球化学参数多维空间中的点云。该方法的核心假设是将未知来源视为点过程的实现。为考虑数据的多维特性及特定物理规则，构建了描述来源分布的概率密度函数，这些规则可形成能够解释观测数据的源配置。该分布结合了模型参数分布的先验知识，通过最大化联合概率密度函数对应的配置来估计来源组成。该方法首先在合成数据上进行标定，随后在热液系统实测数据中完成验证。