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

TL;DR

This paper introduces the HUG model, an interaction point process that combines geological knowledge with hydrochemical data to automatically detect multiple groundwater sources, validated on synthetic and real data.

Contribution

The paper proposes a novel interaction point process model that integrates physical rules and prior knowledge for detecting multiple groundwater sources from hydrochemical data.

Findings

Successfully calibrated on synthetic data

Effectively tested on real hydrothermal data

Improves source detection accuracy

Abstract

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.