Coherence‐Based Characterization of a Long‐Period Monochromatic Seismic Signal
Bibliographic record
Abstract
Abstract Continuous seismic data analysis identifies signals related to physical processes within the Earth or on its surface. Characterizing seismic signals yields insights into source processes and Earth's structural features. Global seismic network analysis of long‐period (25–100 s) surface waves has detected seismic events not identified through high‐frequency body wave analysis. However, detecting long‐lasting monochromatic signals with narrow spectral peaks, which carry valuable information about geological and environmental processes, remains challenging on a global scale. We developed a coherence‐based approach to characterize long‐period monochromatic signals on a global scale. In addition to signals originating from the Gulf of Guinea, Vanuatu islands, and a submarine volcano, we observed a previously unidentified signal originating from the Canadian Arctic, likely associated with glacier dynamics. Our approach explores long‐period monochromatic seismic signals in continuous seismic data, providing a foundation for future studies to characterize the physical processes generating these signals on Earth's surface.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".