Diffraction imaging of polygonal faults within a submarine volcanic terrain, Maverick Basin, south Texas
Bibliographic record
Abstract
Abstract Polygonal fault systems are common structural features of intracratonic continental margins. The map-view geometry of these faults became apparent with the use of powerful fault-imaging seismic attributes, such as coherence and curvature. However, these attributes lack the amplitude information necessary for lithological evaluation. We developed a 3D diffraction volume that not only imaged faults but also contained amplitude information. From the unmigrated stack volume, we extracted diffractions that were transformed into amplitude envelope and root-mean-square amplitude volumes. These attributes, together with clay volume (Vclay) data, were extracted along interpreted horizons and fault planes. Crossplots between seismic attributes and Vclay enabled linear relationships between the attributes and Vclay, which were used to infer lithological composition within fault zones. Our results found that, although the fault zones were clay filled, some subvertically inclined clay-poor zones that could serve as permeable pathways were present along the fault planes. In map view, images from diffraction volume were comparable with those obtained from coherence and curvature attributes; however, diffraction images appeared to be busy because of the huge number of diffracted waves embedded in the data. In addition, we found that, although Vclay increases with increasing diffraction energy, no systematic relationship exists between Vclay and curvature, or between Vclay and coherence. As such, curvature and coherence cannot be used to predict lithological distribution within fault zones. Furthermore, we observed that the higher the diffraction energies, the higher the fluid saturation, suggesting higher impedance contrast at the diffraction points. Therefore, we determined that by analyzing diffraction data, it was possible to infer likely sediment variations that largely control permeability within fault zones.
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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".