Characterizing Seismic Activity From a Rock Cliff With Unsupervised Learning
Bibliographic record
Abstract
Abstract Passive seismic monitoring (PSM) is emerging as a tool for detecting rockfall events and pre‐failure seismicity. In this paper, the potential of PSM for rockfall monitoring is assessed through a case study carried out in Gros‐Morne, Eastern Québec, in a region with prominent roadside cliffs, where more than 500 fallen rocks are found on the main regional road each year. The proposed method relies on using sensitive STA‐LTA windows to detect a very large number of seismic events and build a comprehensive catalog. In total, more than 70,000 seismic events were detected over one year. Gaussian mixtures are used to partition the data set. Based on visual inspection of the data, a main working hypothesis is that the seismic events can be clustered into three groups. After analyzing the spatio‐temporal distribution of the events in each group, we find that the events of one cluster can be associated with anthropogenic activity. The frequency of occurrence of the events of the different clusters and their link with meteorological data is also examined through a regression exercise, to assess the importance of the meteorological variables as explanatory variables. The results allow us to postulate on the physical origins of the signals in the different clusters, attributing them to rockfall activity and wind‐induced seismic noise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".