Joint characterization of heterogeneous conductivity fields and pumping well attributes through iterative ensemble smoother with a reduced-order modeling strategy for solute transport
Bibliographic record
Abstract
Abstract. We develop and test an efficient and accurate theoretical and computational framework to jointly estimate spatially variable hydraulic conductivity and identify unknown pumping well locations and rates in a two-dimensional confined aquifer. The approach (denoted as iES_ROM) integrates an iterative Ensemble Smoother (iES) with a Reduced-Order Model (ROM) for solute transport taking place across an otherwise steady-state groundwater flow field. This offers a computationally efficient alternative to the Full System Model (iES_FSM) upon addressing the high computational demands of ensemble-based data assimilation methods, which typically require large ensemble sizes to characterize uncertainties in (randomly) heterogeneous aquifers. Our iES_ROM is constructed through proper orthogonal decomposition. It is then evaluated across a collection of 28 test cases exploring variations in model dimension, ensemble size, measurement noise, monitoring network, and statistical properties of the (underlying randomly heterogeneous) conductivity field. Our results support the ability of iES_ROM to accurately estimate conductivity and identify pumping well attributes under diverse configurations, attaining a quality of performance similar to iES_FSM. When using moderate ROM dimensions (n = 25–30) and ensemble size (i.e., 500–1000), the accuracy of iES_ROM does not vary significantly while computational time is reduced by nearly an order of magnitude. Our approach thus provides a reliable and cost-effective tool for inverse modeling in groundwater systems with uncertain parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".