Reconstructing the Mesozoic evolution of the Gulf of Mexico Basin: A new model incorporating optimised and focused lithospheric deformation
Bibliographic record
Abstract
The Gulf of Mexico (GoM) is one of the most extensively studied offshore regions, but its Mesozoic evolution remains uncertain. The presence of a thick sedimentary cover and Jurassic salt poses challenges for geophysical imaging, hindering our understanding of the Mesozoic depositional history and crustal architecture evolution. Current tectonic models with rigid plates fail to capture key aspects of GoM evolution. This study introduces a new deformable plate model with optimised focused deformation designed to dynamically adjust stretching factors (SF) during rift evolution. Our model, which calculates crustal thickness and tectonic subsidence (TS) through time and accounts for stretching and thermal subsidence, can explain the depositional history of the pre-salt section and crustal architecture evolution of the GoM. Our model produces a predicted present-day crustal thickness with a root mean square error of 5.6 km with the GEMMA crustal thickness model. The resultant TS of ~1.5 km before the Yucatán block drifted, provides routes for the deposition of red beds through the paleo drainage systems of the northern GoM as successor basin infilling. The model explains ~40 Myrs of missing sedimentary strata, which we attribute to rapid subsidence in the central GoM, shifting red beds deposition beneath the Jurassic salt formations. Extension rate and SF calculations reveal a transition from a magma-rich to a hyperextended margin, with possible mantle exhumation. Our model can be useful in understanding the extent of other Jurassic deposits in the GoM basin and offers a robust framework for comprehending global passive rift margin evolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".