Detection of seismic refraction signals using a variance fractal dimension technique
Bibliographic record
Abstract
Abstract Seismic signals in deep crustal surveys are often contaminated with various types of noise, mainly caused by the low signal-to-noise (S/N) earth environment. A variance fractal dimension (VFD) technique is investigated and tested with real data sets for detection of seismic refraction signals from background noise. The data tested in this study were collected during the 1992 Lithoprobe Abitibi-Grenville Transect high-resolution refraction and wide-angle reflection seismic experiments. The sharpness of transition features on the VFD trajectory is used as a criterion for distinguishing specific seismic phases. The window size and window interval applied in the application of VFD technique were determined using synthetic seismic data for generation of the optimum VFD trajectory. The window size of 48 samples and the window interval of 8 sample intervals were chosen to calculate the fractal dimension values and create the trajectories for detecting phases Pg, Pn, PmP, and ground roll. The VFD technique was also tested and applied for automatic detection of the first breaks in the high-resolution seismic reflection data collected during the 1990 Lithoprobe regional and high-resolution seismic surveys. The sharp transition features corresponding to the first arrivals in the seismic reflection data are distinct and provide us with a robust and powerful tool for separating the seismic signals from noise.
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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 | 0.002 | 0.001 |
| 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 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".