Bibliographic record
Abstract
Abstract Estimates of seismic coherence of 3-D data sets have provided a radically new way of delineating detailed structural and stratigraphic features. Covariance matrices provide the natural formalism to extend the original three-trace crosscorrelation algorithm to larger analysis windows containing multiple traces, thus providing greater fidelity in low signal-to-noise environments. By use of 3-D phase compensation using Radon transforms, we exploit advances made in the high-resolution multiple signal classification (MUSIC) algorithms, originally developed for the defense industry. All three families of multitrace attributes (coherence, amplitude, and phase) are coupled through the underlying geology, such that we obtain three families of complimentary images of geologic features that result in lateral changes in wave form. The phase attributes of dip/azimuth and curvature allow us to image areas that have undergone folding or draping that can not be seen on coherence or amplitude images. The amplitude attributes allow us to image oil/water contacts or other areas of amplitude variation that may not be seen on coherence or dip/azimuth images. Coupled with coherence and the conventional seismic data, these new multitrace dip and amplitude data cubes can greatly accelerate the interpretation of the major features of large 3-D data volumes. At the reservoir scale, they will be of significant help in delineation of subtle internal variations of lithology, porosity, and diagenesis. In computer-assisted interpretation, we strongly feel these new attributes will become the building blocks for the application of modern texture analysis and segmentation algorithms to the delineation of geologic features.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".