A Novel 2.5D Deep Network Inversion of Gravity Anomalies to Estimate Basement Topography
Bibliographic record
Abstract
Summary A novel 2.5D intelligent gravity inversion technique has been developed to estimate basement topography. A deep neural network (DNN) is used to address the fundamental nonuniqueness and nonlinearity flaws of geophysical inversions. The training data set is simulated by adopting a new technique. Using parallel computing algorithms, thousands of forward models of the subsurface with their corresponding gravity anomalies are simulated in a few minutes. Each forward model randomly selects the values of its parameter from a set of predefined ranges based on the geological and structural characteristics of the target area. A DNN model is trained based on the simulated data set to conduct the nonlinear inverse mapping of gravity anomalies to basement topography in offshore Abu Dhabi, United Arab Emirates. The performance of the trained model is assessed by making predictions on noise-free and noise-contaminated gravity data. Eventually, the DNN inversion model is used to estimate the basement topography using pseudogravity anomalies. The results show the depth of the basement is between 7.4 km and 9.3 km over the Ghasha hydrocarbon reservoir. This paper is the 2.5D and improved version of the research (SPE-211800-MS) recently presented and published in the Abu Dhabi International Petroleum Exhibition & Conference (31 October–3 November 2022) proceedings.
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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 | 0.000 |
| 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 | 0.001 |
| 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".