The Source-Scanning Algorithm: mapping the distribution of seismic sources in time and space
Bibliographic record
Abstract
We introduce a new method, which we call the Source-Scanning Algorithm (SSA), for imaging the distribution of seismic sources in both time and space. Using trial locations and origin times, the method calculates the ‘brightness’ function by summing the absolute amplitudes observed at all stations at their respective predicted arrival times. The spatial and temporal distribution of sources is then identified by a systematic search throughout the model space and time for the maximum brightness. The greatest advantages of this method are that: (1) it exploits waveform information (both arrival times and relative amplitudes) without the need to calculate high-frequency synthetic seismograms; and (2) it requires neither pre-assembled phase-picking data nor any a priori assumptions about the source geometry. A series of tests using synthetic data have shown that this method is robust and can faithfully recover the input source configuration to within 1 grid interval. Finally, we demonstrate the value of the algorithm by locating a typical tremor event with emergent waveforms that occurred during the recent episodic tremor and slip (ETS) sequence in the northern Cascadia subduction zone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".