Identification of shallow geohazard channels based on discontinuity seismic attributes in the South Caspian Sea
Bibliographic record
Abstract
ABSTRACT Identification of geomorphological features in seismic data is a key element of seismic interpretation. Channels in the shallow subsurface are potential geohazards. At deeper levels, they can be the actual targets for (horizontal) drilling. Either way, it is important to optimally delineate these features prior to well location positioning and drilling. We have studied a poststack 3D seismic data from the South Caspian Sea featuring shallow channels that are considered potential geohazards for drilling operations. In the first step, we attenuate the acquisition footprints along the inline direction using a geostatistics approach based on factorial kriging. To better visualize channels in the presence of stratigraphic dips, we create a dense set of horizons using an inversion-based flattening algorithm. In the next step, we compare various discontinuity attributes such as semblance, similarity, curvature, and the relatively new attribute based on the multiscale and multidirectional shearlet transformation to determine which one best images our features of interest. Curvature attributes clearly image channel levies (positive curvature) and channel centers (negative curvature). Lateral changes in the curvature magnitude infer sedimentation from the north. Similarity, semblance, and shearlet transform attributes also successfully delineate channel edges, but these attributes do not contain additional geologic information. In the final step, we qualitatively analyze channel thickness variations by the red-green-blue blending of three spectral components based on short window Fourier transforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".