Ambient noise tomography for geothermal exploration: the central Vienna basin, Austria
Bibliographic record
Abstract
The Vienna Basin (VB) is currently the main target area for deep geothermal exploration in Austria. Knowledge of the subsurface heavily relies on active seismic reflection that are expensive and logistically demanding. Affordable geophysical prospecting methods are needed to reduce subsurface uncertainty. Over the recent years, seismic ambient noise tomography (ANT) has proven to be a cost-effective and environment-friendly exploration technique. Here, we present an ANT of the central Vienna Basin revealing the shear-wave velocity structure of the top 5 km beneath the surface. We deployed an array of ~100 seismic nodal instruments during 6 weeks over summer 2023. We measured fundamental-mode Rayleigh and Love-wave group velocity dispersion from seismic ambient noise and employed transdimensional Bayesian tomography to invert for isotropic group velocity maps at periods ranging from 0.8 to 5.5 s. We then extracted Rayleigh and Love group velocity dispersion curves from the group velocity maps at all locations and jointly inverted them for shear-wave velocity as a function of depth using a transdimensional Bayesian framework. We discuss features observed in our 3D shear-wave velocity model relevant to geothermal exploration.
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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 | 0.001 |
| 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".