Efficient Trans-Dimensional Gravity Inversion of Salt Structures Offshore Abu Dhabi
Bibliographic record
Abstract
Abstract This work investigates nonlinear inversion of gravity data to infer Infracambrian Hormuz salt structures offshore Abu Dhabi, UAE. A Bayesian approach with a trans-dimensional parametrization of the subsurface is applied that does not require regularization, resulting in more objective inversion results. The trans-dimensional parametrizations discretize the subsurface structure including the salt dome by an irregular grid of Voronoi cells. Both the number of cells and the cell coordinates are unknown parameters estimated from gravity data. The density contrast of the salt structures is assumed as known. The solution in Bayesian inversion is given by a large ensemble of parameter sets. Here, the trans-dimensional ensemble is obtained with the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm. Residual errors are parametrized by a full covariance matrix, which is estimated and updated as part of an iterative inversion scheme. Efficient rjMCMC sampling is achieved with parallel tempering. Inversion of airborne gravity anomalies illustrates well-defined Infracambrian Hormuz salt structures offshore Abu Dhabi, where the irregular grid spatially adapts to the data information and without the need to impose explicit regularization or fixed grids. Uncertainty estimates highlight salt dome extent. This study provides new insight into the existence and shape of oil reservoirs associated with the underlying salt structures.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".