COMPARISON OF DIFFERENT SEISMIC FILTERING TECHNIQUES ON PRESTACK INVERSION FOR PENOBSCOT AREA-NOVA SCOTIA
Bibliographic record
Abstract
The goal of this study is to compare three different type of seismic filtering according to their inversion results and their quality of data improvements. To do this bandpass filter, Inverse Q, and Radon transform are applied to the original NMO corrected pre-stack data from Nova-Scotia offshore Canada. The seismic data used was provided as pre-stack data of generally good quality. The test for quality of data improvement comes from the results of inversion based on different types of filtering performed on the pre-stack gathers. Bandpass filter, Inverse Q, and Radon transform are applied to the migrated prestack data, over the time range of 0-6000 ms. The Radon transform yields a better seismic image than the bandpass and inverse Q filters, since it removes the noise and the multiples from the data quite efficiently. The respective data volumes were inverted for acoustic impedance using simultaneous prestack inversion. The Radon filtered data provided the best inversion results, based on the continuity of layers and lack of apparent artifacts or noise. The Radon filter does not appreciably alter the frequency content of the data while removing events with moveout inconsistent with primary arrivals. It is likely that the processed data provided originally contained an excellent wavelet, and the other filters were unable to improve upon it, but did diminish the information present, particularly at the lower frequencies, decreasing the quality of the inversion results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".