Toward the calibration of 2D thermomechanical simulations of magma poor passive continental margins: method, validation and case example
Bibliographic record
Abstract
In recent decades, geodynamic modelers have aimed to comprehend key factors governing continental rifting, such as the extension rate, lithospheric thickness, Moho mechanical coupling, and mantle convection’s thermal influence. While prior models offered insights into rifting processes, they lacked the calibration to specific Earth regions. Introducing heterogeneities into the model does in some cases help to calibrate the simulation results to a geological data from a specific region. Acknowledging structural inheritance as a form of kinematic forcing in the models, and recognizing the challenge of anticipating and identifying all inherited geological structures present before rifting, a new modeling approach was devised. This method integrates a new kinematic module into the pTatin2D code, allowing for calibrating numerical simulations with regional geological and geophysical dataset over time while solving for mechanical balance using Stokes flow to ensure that crustal deformation remains consistent with mantle dynamics. By calibrating against a 2D cross-section extracted from the final state of a 3D model, we show that the approach predicts thermal history and deformation paths beyond calibration points. In particular, the thermo-mechanical feedback can help mitigate some uncertainties in the deformation path. Applied to Iberia–Newfoundland margins, the method demonstrates effectiveness in real-case scenarios, aligning with previous reconstructions by incorporating faults and lower crustal flow.
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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) | 0.002 | 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 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".