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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".