Hug model: parameter estimation via the {ABC} {Shadow} algorithm

Christophe Reype and Radu S Stoica and Didier Gemmerle and Antonin Richard and Madalina Deaconu. ( 2023 )
in: 2023 {RING} meeting, pages 8, ASGA

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.

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BibTeX Reference

@inproceedings{reype_hug_RM2023,
 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.},
 author = {Reype, Christophe and Stoica, Radu S and Gemmerle, Didier and Richard, Antonin and Deaconu, Madalina},
 booktitle = {2023 {RING} meeting},
 language = {en},
 pages = {8},
 publisher = {ASGA},
 title = {Hug model: parameter estimation via the {ABC} {Shadow} algorithm},
 year = {2023}
}