On the strength of the phase cross-correlation in retrieving the Green’s function information in a region affected by persistent aftershock sequences
Bibliographic record
Abstract
Abstract Although research on seismic interferometry is now entering a phase of maturity, earthquakes are still the most troublesome issues that plague the process in real applications. To address the problems that arise from spatially scattered and temporally transient enormous earthquakes, preference is usually given to the use of time-dependent weights. However, small earthquakes can also have a disturbing effect on the accuracy of interpretations if they are persistently clustered right next to the perpendicular bisector of the line joining station pairs or in close proximity to one of the stations. With regard to the suppression of these cluster earthquakes, commonly used solutions for dealing with monochromatic microseismic cluster events (e.g., implementing a band-reject filter around a comparatively narrow frequency band or whitening the amplitude spectra before calculating the cross-spectrum between two signals) may not have the necessary efficiency since earthquake clusters are generally a collection of events with different magnitudes, each having its own frequency and energy contents. Therefore, the only solution left in such a situation is to use stronger non-linear time-dependent weights (e.g., square of the running average or one-bit normalization), which may cause Green’s function amplitude information to be lost. In this paper, by simulating the records of a benchmark earthquake MN5.2 with the help of empirical Green’s functions (EGF) obtained after the Ahar-Varzeghan Earthquake Doublet (MN6.4 and MN6.3), it is shown that the amplitude-unbiased phase cross-correlation is a relatively efficient approach in the face of the issues concerning long-standing cluster events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".