Source Characteristics of Tectonic and Induced Events near St. Gallen, Switzerland: Utilizing the Empirical Green’s Function Method and Generalized Inversion Technique
Bibliographic record
Abstract
ABSTRACT Stress drop is a kinematic source parameter essential for understanding the physical mechanisms behind natural or human-induced seismic events, contributing to ground shaking and seismic hazards. This study aims to estimate the Brune stress drop of earthquakes across Switzerland for the years 2013–2014. The geothermal energy project in St. Gallen induced 347 events with a maximum magnitude of ML 3.5, providing an opportunity to quantitatively compare the physical differences between induced and tectonic earthquakes in the surrounding region. Using the S-wave Fourier amplitude spectra of 82 induced events and 83 tectonic earthquakes, we apply two spectral methods to isolate the source terms of the earthquakes: (1) empirical Green’s functions (EGFs) and (2) the nonparametric generalized inversion technique (GIT). GIT is applied to all the induced and tectonic earthquakes, whereas six of the induced events (ML>1.4) with appropriate EGF events are also analyzed using the EGF method. For these six events, both methods yield consistent average and median stress-drop values: 16.4 and 18.1 MPa for EGF, and 14.4 and 14.3 MPa for GIT, respectively, which are a factor of 10 larger than those of induced earthquakes. This discrepancy is possibly due to the alteration of the stress state under the influence of pore fluid in the geothermal setting. Interestingly, we observe that the stress drop increases with seismic moment for both induced and tectonic events, which is consistent with previous findings, though we note the narrow magnitude range in this study. Such nonself-similar scaling, if true, may be related to the depth dependence of the stress drops for tectonic events and the perturbation of pore pressure for induced events.
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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) | 0.001 | 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".