Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.23932 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917299635617792 |
|---|---|
| author | Bretti, Gabriella Ceseri, Maurizio Onofri, Elia Paoluzzi, Matteo |
| author_facet | Bretti, Gabriella Ceseri, Maurizio Onofri, Elia Paoluzzi, Matteo |
| contents | This paper investigates the structural identifiability and a comparative calibration of four water retention curves (WRCs) within the framework of the Richards equation coupled with Darcy's law for capillary imbibition in porous media. The considered models -- two classical physically-based laws and two abstract parametrisations developed for building stones -- are consistently reformulated by expressing the hydraulic conductivity $K(Θ)$ and capillary pressure $ψ(Θ)$ independently, allowing the nonlinear diffusion coefficient $D(Θ)$ to be reconstructed in a unified structural form. This common representation enables a rigorous mathematical comparison across models with different theoretical foundations.
All models are calibrated against the same experimental imbibition dataset using a grid-based optimisation strategy with adaptive refinement. The analysis reveals a structural property of the associated inverse problem: the hydraulic conductivity and the capillary pressure scale enter the governing equation multiplicatively and therefore cannot be independently identified from imbibition data. Only their product acts as an observable diffusion parameter, where model discrimination is primarily governed by the shape of the resulting effective diffusion function.
To the best of our knowledge, this is the first study providing a coherent cross-calibration of these WRCs against an identical dataset within a unified computational framework. Our open-source implementation, released within the Stoneverse platform, provides a reproducible baseline for further developments, including probabilistic inversion and learning-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23932 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Structural Identifiability and Comparative Calibration of Water Retention Curves for Imbibition in Porous Media Bretti, Gabriella Ceseri, Maurizio Onofri, Elia Paoluzzi, Matteo Dynamical Systems 35K65, 76S05 This paper investigates the structural identifiability and a comparative calibration of four water retention curves (WRCs) within the framework of the Richards equation coupled with Darcy's law for capillary imbibition in porous media. The considered models -- two classical physically-based laws and two abstract parametrisations developed for building stones -- are consistently reformulated by expressing the hydraulic conductivity $K(Θ)$ and capillary pressure $ψ(Θ)$ independently, allowing the nonlinear diffusion coefficient $D(Θ)$ to be reconstructed in a unified structural form. This common representation enables a rigorous mathematical comparison across models with different theoretical foundations. All models are calibrated against the same experimental imbibition dataset using a grid-based optimisation strategy with adaptive refinement. The analysis reveals a structural property of the associated inverse problem: the hydraulic conductivity and the capillary pressure scale enter the governing equation multiplicatively and therefore cannot be independently identified from imbibition data. Only their product acts as an observable diffusion parameter, where model discrimination is primarily governed by the shape of the resulting effective diffusion function. To the best of our knowledge, this is the first study providing a coherent cross-calibration of these WRCs against an identical dataset within a unified computational framework. Our open-source implementation, released within the Stoneverse platform, provides a reproducible baseline for further developments, including probabilistic inversion and learning-based approaches. |
| title | Structural Identifiability and Comparative Calibration of Water Retention Curves for Imbibition in Porous Media |
| topic | Dynamical Systems 35K65, 76S05 |
| url | https://arxiv.org/abs/2602.23932 |