Ground-penetrating radar attenuation compensation by Gabor deconvolution: Seismogenic fault imaging at Castelluccio di Norcia (Central Italy)
Bibliographic record
Abstract
ABSTRACT We test a seismic nonstationary Gabor deconvolution (GD) algorithm on synthetic and experimental ground-penetrating radar (GPR) profiles to evaluate how well this algorithm increases vertical resolution and removes attenuation effects from GPR data. Our field data set has been collected across a seismogenic fault in Central Italy, detecting this tectonic structure several years before the 2016–2017 seismic sequence which struck the region and produced coseismic ruptures along the same fault trace. We find that GPR mixed-phase data respond very well to the application of GD in comparison with the conventional and more standard Wiener-spiking deconvolution workflows. We observe a clear increase of the coherence and sharpness of reflection events as well as of hyperbolic diffractions in the fault zone. Gabor-processed GPR data significantly increase the GPR potential to image active Quaternary faults, therefore contributing to the definition of seismotectonic context and to seismic hazard assessment of a study region. We propose the use of the GD to increase interpretability of GPR profiles not only for the identification of tectonic structures but also to achieve high-quality images of the near surface in many GPR applications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".