Case study: Phase-component amplitude variation with angle
Bibliographic record
Abstract
ABSTRACT In detectable and isolated thin layers below seismic resolution, phase decomposition can theoretically be used to discriminate relatively high-impedance thin-layer responses from low-impedance reservoir responses. Phase decomposition can be used to isolate seismic amplitudes with a particular phase response or to decompose the seismic trace into symmetrical and antisymmetrical phase components. These components sum to form the original trace. Assuming zero-phase seismic data and normal American polarity, seismically thin layers that are high impedance relative to overlying and underlying half-spaces are seen on the +90° phase component, whereas a relatively low-impedance thin layer will appear on the −90° phase component. When such phase decomposition is applied to prestack attributes on a 2D line across a thin, 8 m thick, gas-saturated reservoir in the Western Canadian Sedimentary Basin of Alberta, Canada, amplitude-variation-with-angle is magnified on the −90° phase component. The −90° far-offset component allows the lateral extent of the reservoir to be better delineated. This amplification is also seen on the −90° phase component of the gradient attribute. These results are corroborated by seismic modeling that indicates the same phase-component relationships for near- and far-angle stacks as are observed on the real data. Fluid substitution and seismic modeling indicate that, relative to full-phase data, the mixed-phase response observed in this study exhibits variations in fluid effects that are magnified and better observed at far angles on the −90° phase component.
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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".