Hug model: parameter estimation via the ABC Shadow algorithm

TL;DR

This paper introduces the use of the ABC Shadow algorithm to estimate parameters of the Hug model, a point process for analyzing geological fluid mixing, aiming to enable fully unsupervised source detection.

Contribution

It demonstrates how the ABC Shadow algorithm can be applied to derive priors for the Hug model parameters, advancing unsupervised geological source analysis.

Findings

Successful parameter estimation for the Hug model using ABC Shadow

Potential for integrating geological expertise into unsupervised models

Enhanced understanding of water source interactions

Abstract

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.