Seismic Imaging of the Southern Vienna Basin (Austria) Using Probabilistic Ambient‐Noise Tomography
Bibliographic record
Abstract
ABSTRACT Surface‐wave ambient noise tomography has proven to be a cost‐effective and reliable tool for imaging sedimentary basins when coupled with dense nodal seismic arrays. Here, we deployed 181 seismic nodes in two asynchronous phases across the southern Vienna Basin in spring 2024. We retrieve fundamental‐mode Rayleigh and Love wave group velocity dispersion curves from seismic noise cross‐correlations. We then obtained a pseudo three‐dimensional (3D) model and a seismic radial anisotropy () model of the area from a 2‐step approach that employs trans‐dimensional probabilistic (Bayesian) inference. The 3D model highlights the structure of the Neogene basin. The 3D seismic radial anisotropy reveals several patterns, which may help constrain the presence and nature of faults and geologic fabrics in the study area. Combined, these models constrain first‐order features of the basin structure that will be useful for planning further geothermal exploration. In particular, this work guides future detailed, spatially targeted two‐dimensional/3D seismic reflection surveys.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".