Coherence attribute applications on seismic data in various guises — Part 1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The iconic coherence attribute is very useful for imaging geologic features such as faults, deltas, submarine canyons, karst collapse, mass-transport complexes, and more. In addition to its preconditioning, the interpretation of discrete stratigraphic features on seismic data is also limited by its bandwidth, where in general the data with higher bandwidth yield crisper features than data with lower bandwidth. Some form of spectral balancing applied to the seismic amplitude data can help in achieving such an objective so that coherence run on spectrally balanced seismic data yields a better definition of the geologic features of interest. The quality of the generated coherence attribute is also dependent in part on the algorithm used for its computation. In the eigenstructure decomposition procedure for coherence computation, spectral balancing equalizes each contribution to the covariance matrix, and thus it yields crisper features on coherence displays. There are other ways to modify the spectrum of the input data in addition to simple spectral balancing, including the amplitude-volume technique, taking the derivative of the input amplitude, spectral bluing, and thin-bed spectral inversion. We compare some of these techniques, and show their added value in seismic interpretation, which forms part of the more elaborate exercise that we had carried out. In other work, we discuss how different spectral components derived from the input seismic data allow interpretation of different scales of discontinuities, what additional information is provided by coherence computed from narrow band spectra, and the different ways to integrate them.
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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) | 0.001 | 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 it