Characterizing fault features in low-density seismic coverage areas using the satellite gravity and EMAG magnetic data: A case study of the southeastern Gulf of Mexico basin
Bibliographic record
Abstract
Abstract Seismic data can provide an intuitive and accurate reflection of stratigraphic information. However, in areas with low-density seismic line coverage, relying solely on seismic profiles to accurately describe the spatial distribution characteristics of faults in the study area is not convincing. This study used two boundary identification methods of gravity and magnetic potential fields: analytical signal amplitude and mean normalized total horizontal derivative, to identify the boundaries of geological bodies in the southeastern Gulf of Mexico basin, based on the lateral heterogeneity of geological structures. Combined with the interpretation results of seismic profiles, the accuracy of the potential field boundary identification was verified, enhancing the rationality of joint gravity, magnetic, and seismic interpretation results for studying the spatial distribution characteristics of faults. The study confirmed that the analytical signal amplitude and the mean normalized total horizontal derivative methods can be effectively applied to fault characterization in areas with insufficient seismic coverage. Multiscale faults identified using various approaches controlled the stratigraphic deposition during the Jurassic and Early Cretaceous periods. This research implemented a method for enhancing the satellite gravity and magnetic anomalies and provided new insights into studying the sedimentary faults and regional tectonic evolution in the southeastern Gulf of Mexico basin.
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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 | 0.001 | 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".