COMPARISON OF STACKING RESULTS USING CONVENTIONAL AND WEIGHTED TECHNIQUES, AND THEIR AFFECT ON COHERENCE ATTRIBUTE FOR PENOBSCOT SEISMIC DATA OF NOVA SCOTIA, CANADA
Bibliographic record
Abstract
The aim of this thesis is to compare stacking results using conventional and weighted techniques, and illustrate how their results affect coherence attribute for Penobscot seismic data of Nova Scotia, Canada. Pre-processing and necessary basic steps have already been applied to the data by the owner, and the data set was provided to me as NMO corrected, time migrated pre-stack data(PSTM). When conventional and weighted stack methods are applied to the data, multiples are removed, and random noises are suppressed as expected. However, weighted stack method results are better and suppressed noises more effectively. Seismic attributes are used when seismic data does not directly provide enough information about underground. Coherence attribute is one of the useful attribute to identify faults, cracks, stratigraphic structures and their borders. Coherence attribute results calculated from conventional stacked data and weighted stacked data have shown dramatic differences. Both results shows the same events. However, coherence attribute calculated with weighted stacked data makes faults and stratigraphic structures more apparent, clear, and easier to interpret.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".