Static corrections via raypath interferometry: recent field experience
Bibliographic record
Abstract
Summary Raypath interferometry is a processing technique developed to address problems encountered with conventional static corrections methods in areas where basic assumptions used by these methods are violated. It introduces the ‘raypath-consistency’ concept to generalize the usual ‘surface-consistency’ constraint used in conventional statics methods; and it uses interferometry concepts to accommodate uncertain or non-discrete reflection arrivals by replacing event picking and time shifting with crosscorrelation and deconvolution. Raypath interferometry was first successfully applied to a high-resolution seismic line in the MacKenzie Delta, for which surface-consistency was violated by the high-velocity permafrost surface layer, and which manifested instances of multipath arrivals, which contaminate the primary reflection arrivals. Although developed primarily to handle problem lines like the MacKenzie Delta line, raypath interferometry is a generally applicable technique which works equally well on data for which conventional statics methods are also successful. We demonstrate this using a recent 3C seismic survey from the Hussar, Alberta area. Furthermore, we demonstrate raypath interferometry on the radial component (PS) of these data, where the large shear-wave statics appear to be non-stationary.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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".