Seismic velocity structure and seismotectonics of the southern Vienna Basin (Austria) with a large-N nodal deployment
Bibliographic record
Abstract
Abstract In spring 2024, we deployed a large seismic nodal array across the southern Vienna Basin, Austria. Using a machine-learning approach, we constructed a seismic catalog for the 60-day deployment period and reliably identified 100 events. We applied a local earthquake tomography inversion procedure to relocate the detected events and derive three-dimensional P- and S-wave velocity models and V P /V S estimates. The results reveal a low-velocity anomaly with high V P /V S estimates, corresponding to the Neogene basin structure. In contrast, high-velocity anomalies with low V P /V S estimates highlight nappe systems associated with the Alpine Orogen. Most of the relocated seismicity during the deployment is linked to the April 14 th , 2024, M ~ 3 earthquake. The mechanism for this event, along with its aftershock distribution, suggest that the rupture occurred on a normal splay fault of the Vienna Basin Transfer Fault System, situated near its intersection with the basal detachment at depth. As this area is highlighted for its geothermal resource potential, a comprehensive understanding of geological structures and potential hazards is essential for responsible resource development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".