Modeling of the in situ state of stress in elastic layered rock subject to stress and strain-driven tectonic forces
Bibliographic record
Abstract
Abstract. In this study we describe and compare eight different strategies to predict the depth variation of stress within a layered rock formation. This reveals the inherent uncertainties in stress prediction from elastic properties and stress measurements, as well as the geologic implications of the different models. The predictive strategies are based on well log data and in some cases on in situ stress measurements, combined with the weight of the overburden rock, the pore pressure, the depth variation in rock properties, and tectonic effects. We contrast and compare stresses predicted purely using theoretical models with those constrained by in situ measurements. We also explore the role of the applied boundary conditions that mimic two fundamental models of tectonic effects, namely the stress- or strain-driven models. In both models, layer-to-layer tectonic stress variations are added to initial predictions due to vertical variation in rock elasticity, consistent with natural observations, yet describe very different controlling mechanisms. Layer-to-layer stress variations are caused by either local elastic strain accommodation for the strain-driven model, or stress transfers for the stress-driven model. As a consequence, stress predictions can depend strongly on the implemented prediction philosophy and the underlying implicit and explicit assumptions, even for media with identical elastic parameters and stress measurements. This implies that stress predictions have large uncertainties, even if local measurements and boundary conditions are honored.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".