The seismic signature and geothermal potential of the Schwechat Depression in the Vienna Basin, Austria, from ambient noise tomography
Bibliographic record
Abstract
The Schwechat depression, in the Vienna Basin (VB) is currently the main target area for deep geothermal exploration in eastern Austria. Knowledge of the subsurface heavily relies on active seismic reflection profiling experiments that are expensive and logistically demanding. Affordable geophysical prospecting methods are needed to reduce subsurface uncertainty over large spatial areas. Over recent years, seismic ambient noise tomography (ANT) has proven to be a cost-effective and environment-friendly exploration technique fulfilling this need. Here, we present an ANT study of the central Vienna Basin revealing the shear-wave velocity, and shear-wave radial anisotropy structure down to 5 km beneath the surface. We deployed an array of 100 seismic nodal instruments during 5 weeks over summer 2023. We measured fundamental-mode Rayleigh and Love-wave group velocity dispersion from seismic noise correlations, and employed transdimensional Bayesian tomography to invert for isotropic Rayleigh and Love 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 maps at all locations, and jointly inverted them for shear-wave velocity and radial anisotropy as a function of depth using a transdimensional Bayesian framework. Our shear-wave velocity model reveals a basin-like low-velocity feature, interpreted as the seismic signature of the Schwechat depression. Another low-velocity feature is observed beneath the city of Vienna, which could be of great interest for geothermal exploration. The shear-wave velocity radial anisotropy structure indicates a thin negative anisotropy layer in the top 150 meters, likely associated with water-saturated open cracks. Between 150 meters and 1.5 km depth, we observe widespread positive radial anisotropy across the entire study area, corresponding to sub-horizontal layering within the Neogene basin. At greater depths, the Schwechat depression is characterized by positive radial anisotropy, while the edges of the Schwechat depression exhibit negative radial anisotropy due to steeply dipping strata and normal faults responsible for the formation of this major depocenter in the Vienna Basin. • Seismic recordings on a dense nodal array for ambient noise tomography. • 3-D isotropic and anisotropic shear-wave velocity models beneath the central Vienna basin, Austria. • The subsurface extent and structure of the Schwechat depression informs on geothermal potential in the central Vienna basin, Austria.
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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 | 0.000 |
| 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 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".