Fine-scale heterogeneous structure impact on the scale-dependency of the effective hydro-electrical relations of unsaturated soils

Moreno, Ziv

doi:10.1016/j.advwatres.2022.104156

Cited by 4 publications

(3 citation statements)

References 65 publications

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“…Neglecting the surface conductivity can be valid for coarse grain soils, or high pore-water EC (Friedman, 1998(Friedman, , 2005Samouëlian et al, 2005). We further simplify the examined cases by neglecting uncertainties in the petrophysical relations that can arise from the soil heterogeneous structure (Moreno, 2022).…”

Section: Electric Flowmentioning

confidence: 99%

“…Both cases are complex and nonlinear processes that pose challenges for modeling, especially due to their dependence on the initial and boundary conditions (Assouline, 2013). Prior investigations to describe water flow via PINNs in unsaturated conditions have been conducted so far on a 1D vertical domain (Bandai & Ghezzehei, 2021, 2022Depina et al, 2022;Wang et al, 2023) with direct measurements of water content and/or matric head or temperature. Here, we considered a 2D domain and also incorporated solute dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data

Haruzi

Moreno

2023

Water Resources Research

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Section: Electric Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data

Haruzi

Moreno

2023

Water Resources Research

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“…Numerous studies have shown that the electrical conductivity of the soil is closely related to the water content of the soil [11][12][13][14][15]. Rainfall can change the water content of landslides in both space and time, and because water content and resistivity are correlated, it will also change the resistivity of landslides.…”

Section: Introductionmentioning

confidence: 99%

Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation

Bai

Zhu

et al. 2022

Remote Sensing

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Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides.

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Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions

Chakraborty,

Rabinovich,

Moreno

2024

Advances in Water Resources

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Fine-scale heterogeneous structure impact on the scale-dependency of the effective hydro-electrical relations of unsaturated soils

Cited by 4 publications

References 65 publications

Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data

Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data

Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation

Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions

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