arXiv subjects

Christophe Reype

Publications and source records attributed to Christophe Reype.

3 recordsLinked to original sources

arXiv2023

Hug model: parameter estimation via the ABC Shadow algorithm

Studying geological fluids mixing systems allows to understand interaction among water sources. The Hug model is an interaction point process model that can be used to estimate the number and the chemical composition of the water sources involved in a geological fluids mixing system from the chemical composition of samples Reype (2022); Reype et al. (2020, 2022). The source detection using the Hug model needs prior knowledge for the model parameters. The present work shows how the parameter estimation method known as the ABC Shadow algorithm Stoica et al. (2021, 2017) can be used in order to construct priors for the parameters of the Hug model. The long term perspective of this work is to integrate geological expertise within fully unsupervised models.

physics.geo-ph↗

arXiv2023

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

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.

stat.AP↗

arXiv2020

Bayesian statistical analysis of hydrogeochemical data using point processes: a new tool for source detection in multicomponent fluid mixtures

Hydrogeochemical data may be seen as a point cloud in a multi-dimensional space. Each dimension of this space represents a hydrogeochemical parameter (i.e. salinity, solute concentration, concentration ratio, isotopic composition...). While the composition of many geological fluids is controlled by mixing between multiple sources, a key question related to hydrogeochemical data set is the detection of the sources. By looking at the hydrogeochemical data as spatial data, this paper presents a new solution to the source detection problem that is based on point processes. Results are shown on simulated and real data from geothermal fluids.

stat.ME↗