Hydraulic tomography using temporal moments of drawdown recovery data: A laboratory sandbox study
Bibliographic record
Abstract
Hydraulic tomography (HT) is a new technology that images the hydraulic heterogeneity of the subsurface. Unlike steady state hydraulic tomography (SSHT), which provides a hydraulic conductivity (K) tomogram, transient hydraulic tomography (THT) provides reliable tomograms of both K and specific storage (Ss). Effective as it may be, THT is a computationally demanding technique. To ease the computational burden, a HT which utilizes zeroth and first temporal moments of transient drawdown recovery data (HT‐m) has been developed by Zhu and Yeh (2006). This procedure simplifies the governing equation from a single parabolic equation to two Poisson's equations for the zeroth moment and characteristic time defined as the ratio between the first and zeroth moments. The approach was previously tested using synthetic simulations. The numerical experiments tested the feasibility of HT‐m under ideal conditions, where measurements and model are assumed to be free of error. In this paper, we further evaluate the performance of HT‐m using cross‐hole pumping tests conducted in a heterogeneous, synthetic aquifer constructed in a laboratory sandbox in more realistic situations, where the data used in the inversion are not free of experimental errors. Unlike field tests, the laboratory tests were conducted in a synthetic aquifer created with a prescribed heterogeneity pattern and all forcing functions (initial and boundary conditions and source‐sink terms) controlled. Results from the HT‐m approach were compared to those from THT previously conducted by Liu et al. (2007). Our results show that the estimation of the K tomogram using the HT‐m approach is reasonable, but the estimation of the Ss tomogram is unreliable in comparison to the THT approach.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".