Uncertainty quantification of in situ horizontal stress with pressuremeter using a statistical inverse analysis method
Bibliographic record
Abstract
Knowledge of in situ stress magnitude and orientation plays a very important role in geological/geotechnical engineering and in the development of energy resources, such as caprock integrity, waste fluid disposal, geological storage of CO2, and geothermal energy extraction. The uncertainty of estimated parameters, especially horizontal stress, from in situ tests such as pressuremeter tests is a long-standing challenge owing to the existence of uncertainties from geomaterial spatial variability, measurement errors, limited information, and modelling methods. Therefore, non-unique solutions are often encountered in pressuremeter interpretation. In this study, a statistical inverse analysis method is proposed to solve this issue by combining a closed-form solution, finite-difference model, and selected optimization algorithms. The objective of the statistical inverse analysis is to determine the optimal parameters by minimizing the sum of squared errors while providing the confidence intervals of inversed parameters. Random variables generated in the optimization process reproduce the potential parameter uncertainties. The Jacobian matrix and confidence intervals are derived from the optimization process to evaluate variability of the predicted horizontal stress and ground properties. A workflow is presented that demonstrates a statistical inverse method for analyzing pressuremeter results and helps quantify uncertainties of the ground properties and in situ stress magnitudes and orientations derived from a pressuremeter test.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".