Detection of Low‐Frequency Earthquakes in Broadband Random Time Sequences: Are They Independent Events?
Bibliographic record
Abstract
Abstract Low‐frequency earthquakes (LFEs) are detected primarily from continuous seismograms using a matched‐filter technique with an impulsive template waveform in a relatively narrow frequency band. However, this method can also detect events from some kinds of random time sequences without clearly isolated events. Here this fact is demonstrated via simple numerical simulations using the synthetic moment accelerations from a model of broadband slow earthquake, the Brownian slow earthquake (BSE) model, and a totally random noise sequence. The matched‐filter technique identifies time sections including relatively isolated pulse‐like fluctuations, as signals in both time sequences, depending on the threshold. These waveforms, stacked relative to the signal timing, show a clear impulse similar to the assumed template in both time sequences, which highlights that we might potentially misinterpret an original time sequence as containing many isolated pulse‐like events. An important difference exists between the BSE model and random noise at frequencies much lower than the analyzed frequency band, with the stacked BSE sequence containing coherent signals at very low frequencies, which are not visible in the noise. Real observations in the Cascadia subduction zone also contain similar coherent signals at low frequencies, suggesting that these LFE signals are coincident with some slow slip. Therefore, so‐called LFEs might potentially be a misinterpretation due to signal processing, or at least they are the tip of the iceberg, with these signals forming a component of a very broadband slow‐earthquake‐like slip process that possibly occurs over sub‐second to multi‐year timescales.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".