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This paper presents a new interaction point process that integratesgeological knowledge for the purpose of automatic sources detection of multiplesources in groundwaters from hydrochemical data. The observations areconsidered as spatial data, that is a point cloud in a multi-dimensional spaceof hydrogeochemical parameters. The key hypothesis of this approach is toassume the unknown sources to be the realisation of a point process. Theprobability density describing the sources distribution is built in order totake into account the multi-dimensional character of the data and specificphysical rules. These rules induce a source configuration able to explain theobservations. This distribution is completed with prior knowledge regarding themodel parameters distributions. The composition of the sources is estimated bythe configuration maximising the joint proposed probability density. The methodwas first calibrated on synthetic data and then tested on real data fromhydrothermal systems.

HUG model: an interaction point process for Bayesian detection of multiple sources in groundwaters from hydrochemical data