发表机构
School of Geosciences and Info-physics, Central South University; School of Earth, Environment, and Sustainability, University of Iowa; School of Management Science and Engineering, Hunan University of Technology and Business（中南大学地球科学与信息物理学院; 爱荷华大学地球、环境与可持续发展学院; 湖南工商大学管理科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于自动微分的 GPU 加速框架 AD-TLERT，整合多模块为单一计算链，实现时间推移 ERT 反演，加速约51倍，可直接反演含水量，野外应用可成像融雪驱动的山坡湿润过程。
AI 中文摘要
时间推移电阻率层析成像（TL-ERT）可提供 subsurface 水文变化的空间分布信息，但长监测序列的反演计算量大，修改数据拟合度、正则化、模型参数化或岩石物理变换时，还需新的梯度推导和单独实现。本文提出 AD-TLERT，这是一种基于自动微分的、统一的 GPU 加速时间推移 ERT 反演框架，将模型参数化、可微岩石物理变换、正演模拟、数据拟合度、正则化及辅助约束整合为单一计算链，不同反演方案可复用同一 PDE 导数实现，无需为每种情况重新推导完整 ERT 灵敏度。与 pyGIMLi 的对比显示，正演响应、梯度及恢复的电阻率模型吻合度高；在测试配置下，AD-TLERT 实现约 51 倍加速。合成实验表明，反演选择会影响恢复异常的振幅、几何形态和时间特征；通过嵌入的岩石物理关系传播梯度，AD-TLERT 可直接反演含水量，对测试模型的估计比反演后转换更准确。野外应用进一步证明，可结合 ERT、温度和土壤湿度观测，成像融雪驱动的山坡湿润过程。AD-TLERT 为时间推移 ERT 反演和水文解释提供了高效灵活的框架。
英文摘要
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.
CommentsMain text: 10 figures. Supplementary Information: 2 figures and 2 tables
