Interpolating GPR data using anti‐alias singular spectrum analysis (SSA) method
Bibliographic record
Abstract
ABSTRACT Ground Penetrating Radar data are often acquired along profiles employing bistatic equipment with a fixed distance between the transmitter (Tx) and receiver (Rx) antennae. Even in cases where more than two antennae are used, the number of channels tends to be relatively small, resulting in either a limited number of offsets or gathers with inadequate far offsets. Estimating stacking velocity and performing migration from this type of datasets are difficult. In this paper, we present techniques to interpolate both aliased and non‐aliased datasets in the offset domain and the common‐midpoint domain. The latter permits us to increase the fold of the survey and consequently improve the process of velocity analysis and migration. We assess the reconstruction efficiency of the interpolator using both synthetic and real data to different degrees of decimating. In both cases, the unaliased version of both datasets provides an accurate solution for a careful comparative analysis. At the end of this work, we make a further comparison between the resulting migrated and stacked sections for both the original and reconstructed datasets in order to highlight the efficiency of the interpolation algorithms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".