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Conditional Diffusion Models for Hydrogeologic Parameter Estimation from Electrical Resistivity Tomography Time-lapse Data
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DOI：10.1016/j.advwatres.2026.105312.png)
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Abstract
En 中文
• Introduced Conditional Diffusion Model (CDM) for robust, uncertainty-aware inversion of time-lapse Electrical Resistivity Tomography (ERT) data to estimate hydrogeological parameters. • Designed a robust inversion framework that effectively handles the ill-posed nature of subsurface geophysical inversion while providing reliable uncertainty quantification. • Evaluated the CDM using accuracy, precision, and goodness metrics, confirming its reliability in quantifying uncertainty for 29 hydrogeological parameters. • Demonstrated the ability of forward simulations to accurately reproduce conditional ERT responses based on the estimated hydrogeological parameters.
Keywords:
Conditional Diffusion Model
Electrical Resistivity Tomography
Hydrogeological Parameters
Uncertainty Quantification
Time-lapse Data
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4.2
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4.6K
Citations:
1.5W
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