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

TL;DR

This paper introduces a Bayesian point process approach for source detection in multicomponent hydrogeochemical data, effectively identifying fluid sources from complex, multi-dimensional datasets.

Contribution

It presents a novel Bayesian point process method for source detection in hydrogeochemical data, advancing analysis of multicomponent fluid mixtures.

Findings

Effective source detection demonstrated on simulated data.

Successful application to real geothermal fluid data.

Improved identification of mixing sources in hydrogeochemical analysis.

Abstract

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.