Deconvolution and spatial resolution of airborne gamma-ray surveys
Bibliographic record
Abstract
Abstract The first part of the paper presents a method for frequency domain deconvolution of airborne gamma-ray surveys using a Wiener filter. A geometrical detector model is used to model gamma-ray detection, with aircraft movement simply incorporated by a multiplicative term. The method requires estimation of the autocorrelation functions governing both signal and noise. The former is estimated through the radially averaged power spectrum of the survey data, whereas an error propagation analysis is used to estimate the latter, which is assumed white. Slight manual adjustments to the noise level are used to tune the reconstruction. The technique is applied to a low-altitude radiometric survey collected along closely spaced transects. Results are good for thorium, but are poor for both potassium and uranium. This can be attributed to the high noise levels in the potassium and uranium estimates, principally due to scattered gamma-rays from high thorium concentration. Much better results are obtained when the method is applied to a survey with more typical radioelement concentrations. The reconstructions are improved significantly if an adaptive 2D Lee filter is applied prior to deconvolution. The second part of the paper addresses how noise in the data and attenuation of signal due to the flying height limit the spatial resolution. The autocorrelation functions of signal and noise, along with the gamma-ray model, can be used to determine how signal-to-noise ratio degrades with increasing height. The frequency where signal and noise are present in equal quantity can be used as an estimate of the spatial resolution. Predicted critical sampling rates range from 30 m at 20 m elevation to 60 m at 60 m elevation and 90 m at 120 m elevation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".