Bibliographic record
Abstract
ABSTRACT Correcting reflection seismic data for the effects of near-surface irregularities is a persistent problem usually addressed at least partly by static corrections applied to traces. However, there are areas where static corrections are ineffective because basic assumptions are violated. The assumptions which fail most often are surface consistency and stationarity, which are central to the concept of static corrections. To address this failure, I mapped raw seismic traces into the radial trace domain and gathered the radial traces by common surface angle. Then I imposed a more general constraint, raypath consistency, which simultaneously introduces nonstationarity. Conventional static correction also assumes implicitly that reflection events consist of single discrete arrivals. This is not true, however, in regions where near-surface multipathing and scattering complicate reflection event waveforms. Borrowing from recent work in seismic inferometry, I removed the single-arrival assumption by using trace crosscorrelations to estimate and deconvolve surface functions from traces, rather than applying time shifts. The entire crosscorrelation function is used in every case, so both timing and waveform variations are removed by the deconvolution. The operation is applied in the common-angle domain, so it is raypath consistent and nonstationary. The method, dubbed “raypath interferometry,” was applied successfully to a set of 2D Arctic field data with serious surface consistency and multipath problems, and to a set of 3C 2D land data with very large S-wave receiver statics. Although intended primarily for use on seismic data for which conventional statics corrections fail, raypath interferometry can be used on any seismic data; its assumptions include single-arrival events and surface consistency as special cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".