Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method
Bibliographic record
Abstract
Abstract Groundwater models always involve high‐dimensional parameters, which makes computationally tractable data assimilation using surrogate models very challenging. To address this issue, one common practice is to employ dimension reduction (DR) techniques. Nevertheless, traditional DR methods are usually implemented based on prior parameter statistics, that is, without considering the inherent system dynamics. Here, we show that when significant difference in parameter sensitivity exists, further efficiency can be achieved by adopting a supervised DR method, that is, the active subspace (AS) method. To avoid non‐trivial efforts in calculating the gradient information needed in the standard AS method, a cluster‐based gradient‐free AS (GFAS) method is developed in this study. By combining GFAS with Gaussian process regression, a surrogate model for the CPU‐demanding groundwater model can be adaptively constructed to accelerate data assimilation. Furthermore, a compensation scheme is proposed to cope with uncertainty underestimation caused by DR. The developed approach is tested with numerical experiments and field cases, which illustrated that the new approach is more efficient than the previously developed unsupervised ones by incorporating sensitivity information. Although an iterative ensemble smoother is employed in this study, the proposed method can also be used in other data assimilation approaches, such as Markov chain Monte Carlo and ensemble Kalman filter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".