Data and codes for " A Unified Hybrid Deterministic-Stochastic Inversion Methodology" submitted to Jounal of Hydrology.

Accurate characterization of spatial heterogeneity in hydraulic properties is crucial for reliable groundwater modeling. Conventional approaches face a persistent trade-off between computational efficiency and characterization accuracy. Deterministic inversion with zonation offers computationalefficiencybut oversimplifies subsurface variability. Fully stochastic inversion without zonation or stochastic inversion with zonationcaptures heterogeneityin detail but can be computationally intensive andeven prohibitive for large-scale inverseproblems. To balance inversionaccuracy and computational efficiency, a mixed parameterization configuration is introduced, which involves delineating the model domain into multiple homogeneous and heterogeneous zones. This configuration encompasses diverse heterogeneity patterns such as zonal homogeneity, global heterogeneity, and zonal heterogeneity. Accordingly, a unified hybrid deterministic–stochastic (HDS) inversion methodology is proposed within ageostatistical inversion framework to flexibly support three conventional inversion strategies. When integrated with the Reduced-Order Successive Linear Estimator (ROSLE), the HDS-ROSLE approach achieves dimensionality reduction both conceptually and mathematically. A representative mixed configuration features a leaky aquifer system consisting of two homogeneous aquifers separated by a discontinuous, window-punctuated aquitard. A synthetic hydraulic tomography survey is performed for identifying the aquitard window. Four inversion strategies are implemented via the HDS-ROSLE approach. Comparative analysis reveals that the HDS inversiondelivers data-fitting performance comparable to those of fully stochastic and stochastic zonation inversions. Most significantly, the HDS inversion resolves the localized heterogeneity with lower uncertainty and substantially reduced computational cost. The inherent mixed structure provides a feasible foundation for the HDS inversion methodology to effectively characterize non-Gaussian fields.