Analysis of measured pore pressure response to atmospheric pressure changes to evaluate small-strain moduli: methodology and case studies
Bibliographic record
Abstract
The use of pore pressure responses to fluctuations in total stress (resulting from barometric pressure changes) to calculate moduli and other material properties is a recently developed technique being applied to deep aquitard formations. Initially, the method has relied on visual interpretation of the data from grouted-in piezometers, resulting in a qualitative result with little opportunity to define the quality of measured data; more recently, linear and multiple regression analyses were used to assess the same properties with limited success. Here, a methodology is developed to determine loading efficiency from pore pressure measurements using filtering and numerical regression. The results indicate a near linear relationship between the change in pore pressure and change in stress (barometric pressure), resulting in an estimation of loading efficiency and quantification of the quality of fit. Four to 6 days of data appear to best isolate the barometric fluctuation with the developed filters. The technique is successfully applied to a “simple” site, where groundwater conditions are relatively stable, as well as a complex site, where groundwater conditions are changing due to fluctuating river levels. The successful application to the latter site shows that robust analysis is possible, even for dynamic and complex environments, and that the method represents a viable alternative for estimating material parameters of formations that are historically difficult to characterize.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".