Transient Hydraulic Tomography Analysis of Fourteen Pumping Tests at a Highly Heterogeneous Multiple Aquifer–Aquitard System
Bibliographic record
Abstract
Hydraulic tomography based on geostatistics has proven to be robust in characterizing subsurface heterogeneity in hydraulic conductivity (K) and specific storage (Ss) through the joint inversion of drawdown records from multiple pumping tests. However, the spatially variable estimates can be smooth or even erroneous for areas where pumping/observation data densities are not high. Previous hydraulic tomography surveys conducted at the North Campus Research Site (NCRS) on the University of Waterloo campus in Waterloo, Canada, revealed that the estimated hydraulic parameters were smooth and the known aquitard was erroneously identified as a high K zone. This was likely the consequence of the site being highly heterogeneous, while only utilizing four pumping tests and not having measurable drawdowns in the low K aquitard for inverse modeling. Here, we investigate whether improved K and Ss estimates could be obtained through the inclusion of additional pumping test data by stressing both aquifer and aquitard zones for a sufficiently long period. Specifically, six additional pumping/injection tests were conducted at the site, and a transient hydraulic tomography analysis with 14 tests was completed. Results reveal that there is a significant improvement to the K and Ss tomograms in terms of the visual correspondence with various geologic units, including its connectivity. More importantly, with the availability of additional data, we found that the inverse model now can better capture the high and low K features for nine boreholes when compared with K values obtained from permeameter tests. The estimated K and Ss tomograms are then used for the forward simulation of one additional pumping test not used for model calibration, revealing reasonable predictions. While encouraging results are obtained by including a large number of pumping tests to the transient hydraulic tomography analysis, stratigraphic boundaries are still smoothed, which is a direct consequence of utilizing a geostatistics-based inversion approach that assumes stationarity in statistical properties. To capture such sharp boundaries, incorporation of additional data types, such as geological and geophysical information, may be necessary when data densities are not sufficiently high.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".