Hypocentre determination offshore of eastern Taiwan using the Maximum Intersection method
Bibliographic record
Abstract
The maximum intersection (MAXI) method, which derives from the master station method (MSM), determines within a 3-D velocity model the absolute hypocentral location based on observed arrival times. First, the spatial node that better satisfies the arrival time differences computed at all station pairs, plus or minus an error tolerance value (in seconds), is defined as the preliminary hypocentral solution (PRED). Second, because PRED depends neither on the estimate of origin time nor on the residual root mean square (rms), residual outliers are objectively detected and cleaned out from the original data set without any iterative process or weighting. Third, a statistical minimization (residual rms) is conducted in a small domain around the PRED node, which results in a unique FINAL solution. The MAXI method is applied to the determination of earthquake hypocentres (with the proper station correction terms) in the southernmost extremity of the Ryukyu subduction zone, where several dense seismic clusters occur near the seismogenic plate interface. The location of earthquakes, recorded at both the Taiwanese and Japanese networks, is obtained for about a thousand events (between 1992 and 1997). The process uses a detailed 3-D velocity model based on multiple geophysical data sources obtained in the junction area between subduction and collision (east of Taiwan). The earthquake clustering and the significant drop in residual statistics (1.20, 0.80 and 0.35 s, for Taiwanese catalogue, MSM and MAXIM solutions respectively) indicate the accuracy of the method, which can be used to routinely determine absolute hypocentre location based on observed arrival times.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".