Low‐Rank Geostatistical Inversion for Spatiotemporal Heterogeneity of Aquitard Hydraulic Parameters
Bibliographic record
Abstract
Abstract Many hydrogeological processes are intricately linked to both the spatial heterogeneity and temporal dynamics of the involved parameters. The variability in hydrogeological parameters across both space and time domains is defined as the spatiotemporal heterogeneity. This study introduces a partitioning geostatistical inversion approach to characterize the spatiotemporal heterogeneity, and further applies this methodology to the long‐term multi‐extensometer data from the second aquitard beneath Qingliang Primary School, Changzhou City, China. The Principal Component Geostatistical Approach (PCGA) is used to capture the temporal variability in the hydraulic conductivity ( K ) and specific storage ( S s ) across the three sub‐layers of the aquitard of interest. Comparative analysis reveals that models accounting for spatiotemporal heterogeneity achieve significantly higher accuracy than those considering only spatial heterogeneity. PCGA effectively captures the temporal variations in aquitard K and S s , with sub‐layer 3 dominating the temporal dynamics. Additionally, neglecting the initial delayed drainage within the aquitard leads to a notable overestimation of K . Moreover, utilizing a stage‐wise distribution of parameters as an initial guess significantly enhances inversion accuracy. The proposed methodology not only deepens the understanding of aquitard deformation but also holds broad potential for advancing the characterization of spatiotemporal heterogeneity in 4D hydrogeological modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".