Joint analysis of the 2014 Kangding, southwest China, earthquake sequence with seismicity relocation and InSAR inversion
Bibliographic record
Abstract
Abstract Over 1000 earthquakes struck the northwest of Kangding on the Xianshuihe fault in southwest China between 22 and 29 November 2014, including two largest events of Mw 5.9 and Mw 5.6. The hypocenters of 799 relocated earthquakes suggest that two independent main shock‐aftershock subsequences occurred on the Selaha and Zheduotang branches of the Xianshuihe fault, respectively. Fault slip inversion results from one interferometric synthetic aperture radar (InSAR) interferogram (26 September 2014 to 5 December 2014) show that the Mw 5.9 main shock produced a maximum slip of ~0.47 m at the depth of ~9 km. However, there is no distinct slip associated with the Mw 5.6 main shock. The InSAR determined moment is 2.36 × 1018 Nm with a rigidity of 30 GPa, equivalent to Mw 6.2, which is about twofold the total seismic moment of all the recorded earthquakes during the InSAR time span. This large discrepancy between geodetic and seismic moment estimates indicates that there was probably rapid aseismic afterslip in the 2 weeks following the Mw 5.9 main shock. The released seismic energy of this earthquake sequence is far less than the accumulated strain energy since the 1955 M earthquake on the same fault branch, which implies that the seismic risk on the Selaha‐Kangding segment of the Xianshuihe fault remains high.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".