Interpretation of Regional Magnetic Field Data Offshore Niger Delta Reveals Relationship between Deep Basement Architecture and Hydrocarbon Target
Bibliographic record
Abstract
Directional horizontal derivatives, analytic signal, filtering of magnetic data sets and 3D magnetic modelling incorporating induced and remanent magnetization were performed on low resolution aeromagnetic data to unravel the basement structure and its relationship with hydrocarbon target offshore Niger Delta basin. Forward modelling of the residual magnetic data gave discrete depth values that were exploited to compile the depth to basement (thickness of the sedimentary section) map which highlighted deep depocenters, high blocks and major sedimentary fairways in the study area. The uplifted blocks created the arches while downdropped ones produced the depocenters. The depth to the basement map revealed basement paleotopography which resulted from movement along fault zones. The transformed/enhanced data revealed three potential stress regimes trending NE-SW, N-S and E-W. The NE-SW lineaments are shear zones, more dominant and indicate possible extensions within the African continent of Charcot and Chain oceanic fracture zones. The E-W lineaments are revealed not only in the enhanced maps but also in the total magnetic intensity and residual data sets because they are associated with dykes. The N-S structures are very subtle and are therefore highlighted only in the transformed data. In combination with the E-W structures they are brittle and reactivated structures associated with faults and have significant implications for the tectonic evolution of the Niger Delta and its basin extensions. The transformed data sets, depth to basement map and residual data strongly suggest that jostling of the basement blocks has influenced deposition in the Niger Delta basin. The structural highs (basement highs), basement lows (structural lows) and steep/faulted basement flanks are attractive sites for oil and gas accumulation.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".