An automatic-differentiation framework for time-lapse electrical resistivity tomography inversion of hydrologic dynamics

[2608.14661] An automatic-differentiation framework for time-lapse electrical resistivity tomography inversion of hydrologic dynamics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14661 (cs) [Submitted on 31 Jul 2026] Title: An automatic-differentiation framework for time-lapse electrical resistivity tomography inversion of hydrologic dynamics Authors: Pu Yang , Zhengyang Fang , Yuxin Liu , Xuan Su , Deshan Feng , Hang Chen View a PDF of the paper titled An automatic-differentiation framework for time-lapse electrical resistivity tomography inversion of hydrologic dynamics, by Pu Yang and 5 other authors View PDF HTML (experimental) Abstract: Time-lapse electrical resistivity tomography (TL-ERT) provides spatially distributed information on subsurface hydrologic changes. However, inversion of long monitoring sequences is computationally demanding. Modifying the data misfit, regularization, model parameterization, or petrophysical transformation may also require new gradient derivations and separate implementations. Here, we present AD-TLERT, a unified, GPU-accelerated framework for time-lapse ERT inversion based on automatic differentiation. The framework integrates model parameterization, differentiable petrophysical transformations, forward modeling, data misfit, regularization and auxiliary constraints into a single computational chain. Alternative inversion formulations can therefore reuse the same PDE derivative implementation without re-deriving the complete ERT sensitivity for each case. Comparisons with pyGIMLi showed close agreement in the forward responses, gradients, and recovered resistivity models. Under the tested configuration, AD-TLERT achieved an approximately 51-fold speedup. Synthetic experiments showed that inversion choices affect the amplitude, geometry, and temporal behavior of recovered anomalies. By propagating gradients through the embedded petrophysical relationship, AD-TLERT enabled direct water-content inversion and yielded more accurate estimates than post-inversion conversion for the tested model. A field application further demonstrated how ERT, temperature, and soil-moisture observations can be combined to image snowmelt-driven hillslope wetting. AD-TLERT provides an efficient and flexible framework for time-lapse ERT inversion and hydrologic interpretation. Comments: Main text: 10 figures. Supplementary Information: 2 figures and 2 tables Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.14661 [cs.LG] (or arXiv:2608.14661v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14661 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hang Chen [ view email ] [v1] Fri, 31 Jul 2026 23:41:07 UTC (6,296 KB) Full-text links: Access Paper: View a PDF of the paper titled An automatic-differentiation framework for time-lapse electrical resistivity tomography inversion of hydrologic dynamics, by Pu Yang and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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