Multichannel Deconvolution for Earthquake Apparent Source Time Functions
Bibliographic record
Abstract
Abstract Previous studies of earthquake apparent source time functions (ASTFs) removed propagation effects through seismogram deconvolution with a smaller earthquake known as an empirical Green’s function (EGF). We develop a multichannel deconvolution (MCD) algorithm for recovering ASTFs that does not require an EGF, but instead the availability of two or more earthquakes that share a common Green’s function. Under this condition, ASTFs satisfy U i * S j − U j * S i =0, in which U i and S i are the seismogram and ASTF for a given earthquake. This system can be augmented with a scaling equation and written as Ax = b , in which matrix A comprises the seismograms in a block‐Toeplitz structure and x contains the target ASTFs. We minimize an objective function for this linear system with a Newton‐projection algorithm that honors positivity, causality, and duration constraints. If the earthquakes have a suitable range in magnitude, EGF deconvolution may be used to estimate differences in the duration of the events and to obtain a starting model for the larger ASTF(s). We demonstrate the effectiveness of MCD using synthetic tests and apply it to seven M w ∼5 earthquakes from the Kamaishi sequence, Japan, related to the 2011 Tohoku‐Oki M w 9 event. We demonstrate that MCD is an effective way to recover earthquake ASTFs and that the details of rupture revealed by MCD ASTFs will be useful in furthering our understanding of the earthquake source. Electronic Supplement: Figures of seismograms and multichannel deconvolution (MCD) apparent source time functions (ASTFs) from the Kamaishi earthquake sequence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".