Long-duration seismicity and their relation to Copahue volcano unrest
Bibliographic record
Abstract
Abstract Understanding seismic tremor wavefields can shed light on the complex functioning of a volcanic system and, thus, improve volcano monitoring systems. Usually, several seismic stations are required to detect, characterize, and locate volcanic tremors, which can be difficult in remote areas or low-income countries. In these cases, alternative techniques have to be used. Here, we apply a data-reduction approach based on the analysis of three-component seismic data from two co-located stations operating in different times to detect and analyze long-duration tremors. We characterize the spectral content and the polarization of 355 long-duration tremors recorded by a seismic sensor located 9.5 km SE from the active vent of Copahue volcano in the period 2012–2016 and 2018–2019. We classified them as narrow- (NB) and broad-band (BB) tremors according to their spectral content. Several parameters describe the characteristic peaks composing each NB episode: polarization degree, rectilinearity, horizontal azimuth, vertical incidence. Moreover, we propose two coefficients $$C_P$$ C P and $$C_L$$ C L for describing to what extent the wavefield is polarized. For BB episodes, we extend these attributes and express them as a function of frequency. We compare the occurrence of NB and BB episodes with the volcanic activity (including the level of the crater lake, deformation, temperature, and explosive activity) to get insights into their mechanisms. This comparison suggests that the wavefield of NB tremors becomes more linearly polarized during eruptive episodes, but does not provide any specific relationship between the tremor frequency and volcanic activity. On the other hand, BB tremors show a seasonal behavior that would be related to the activity of the shallow hydrothermal system. Graphical Abstract
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".