Interpretation Using Explicitly Encoded Phase, Amplitude and Fault Data
Bibliographic record
Abstract
Current horizon interpretation techniques are based primarily on the use of seismic reflectivity. While there have been robust algorithms developed to work with seismic data, there are limitations and trade-offs with each of these approaches.In this presentation we investigate the merits of interpretation based on the use of a colour blend comprised of phase, amplitude and fault datasets. The colour blend used in this workflow is a Hue-Saturation-Value (HSV) blend. In this blend the hue/colour is controlled by the instantaneous phase, the saturation is controlled by the amplitude, and the value/blackness is controlled by a fault detect volume.Combining these three datasets provides a greater level of explicit information when interpreting an event. In standard cases this information can be inferred by the interpreter using secondary attribute volumes, or an autotracking algorithm performing extra calculations in the background. However, both of these approaches add extra overhead to the work being performed, reducing efficiency. Explicitly encoding phase, amplitude and fault information allows: Reduced incidence of cycle skippingThe ability to pick on a particular phase angleHonouring of faults in autotrackingIncreased visual information in manual interpretationThese points will be reviewed through the interpretation of a number of 3D seismic datasets, with varying data quality and covering a range of geological settings.
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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".