A brittle failure model for long‐period seismic events recorded at Turrialba Volcano, Costa Rica
Bibliographic record
Abstract
Abstract A temporary seismic network, consisting of 23 broadband and six short‐period stations, was installed in a dense network at Turrialba Volcano, Costa Rica, between 8 March and 4 May 2011. During this time 513 long‐period (LP) events were observed. Due to their pulse‐like waveforms, the hypothesis that the events are generated by a slow‐failure mechanism, based on a recent new model by Bean et al. (2014), is tested. A significant number (107) of the LPs are jointly inverted for their source locations and mechanisms, using full‐waveform moment tensor inversion. The locations are mostly shallow, with depths < 800 m below the active Southwest Crater. The results of the decompositions of the obtained moment tensor solutions show complex source mechanisms, composed of high proportions of isotropic and low, but seemingly significant, proportions of compensated linear vector dipole and double‐couple components. It is demonstrated that this can be explained as mode I tensile fracturing with a strong shear component. The source mechanism is further investigated by exploring scaling laws within the data. The LPs recorded follow relationships very similar to those of conventional earthquakes, exhibiting frequency‐magnitude and corner frequency versus magnitude relationships that can be explained by brittle failure. All of these observations indicate that a slow‐failure source model can successfully describe the generation of short‐duration LP events at Turrialba Volcano.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".