A Comparison of Adaptive Processing Techniques with Nth Root Beam Forming Methods
Bibliographic record
Abstract
Small differences in slowness and azimuth for overlapping phases especially where the branches of the travel-time curve are triplicated must be resolved for a meaningful inversion of array data. A computer program package has been written in FORTRAN IV which enables a user to determine, automatically, the apparent azimuth and slowness of any portion of the seismic wavetrain recorded at various arrays if he has the raw data on digital tape and has access to any modern computer. These programs make use of two methods, (i) adaptive processing, and (ii) Nth root beam forming which have been compared to determine the apparent azimuth and slowness of the seismic wavelets. The former method is performed by cross correlating the signal on each channel with a velocity and azimuth filtered trace in an iterative manner until the convergence takes place. In the latter method the operation is done by delaying the various channels to align a group of arrivals with a particular velocity and azimuth; taking the Nth root of the signal; summing and then raising the result to the Nth power. The value of apparent velocity and azimuth which produces a maximum filtered signal is determined. Experiments with clean and noisy synthetic data have shown that the adaptive processing method is more successful for resolving small differences in apparent velocity and azimuth of overlapping wavelets. It also has an advantage that a set of residuals may easily be obtained from the analysis. The Nth root method is extremely powerful in enhancing the signal to noise ratio at the expense of signal distortion. The computation time for both methods is about the same.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".