Machine-learning-based earthquake locations reveal the seismogenesis of the 2020 <i>M</i>w 5.0 Qiaojia, Yunnan earthquake
Bibliographic record
Abstract
SUMMARY A moment magnitude (Mw) 5.0 earthquake hit Qiaojia, Yunnan, China on 18 May 2020. Its hypocentre is only approximately 20 km away from the Baihetan reservoir, the second largest hydropower station in China. The Baihetan Reservoir is located at the junction of multiple fault zones on the eastern boundary of the Sichuan-Yunnan rhombic block, an area with high background seismic activity. The Baihetan Reservoir was planned to be impounded in April 2021 and the Mw 5.0 earthquake occurred during its water-retaining. Thus, it is critical to investigate the seismogenesis of the Qiaojia Mw 5.0 main shock and evaluate the risk of inducing earthquakes near the Baihetan Reservoir after impoundment. In this study, we built a complete and accurate earthquake catalogue to analyse seismicity in the reservoir area before and after the Mw 5.0 Qiaojia earthquake. We adopted a machine learning-based seismic phase picker, PhaseNet, to automatically detect seismic picks from continuous raw seismic data. Seismic phase picks were associated and located using sequential earthquake association and location methods, including REAL, VELEST and hypoDD. We eventually obtained high-precision locations of 1640 earthquakes by the hypoDD. The distribution of earthquake locations indicates that a concealed fault nearly vertical to the surface accommodated the Mw 5.0 Qiaojia main shock. The majority of its aftershocks is located within a narrow depth range of 8–13 km, indicating that the stresses in the hypocentral area were concentrated near the hypocentre of the Mw 5.0 earthquake. Along with focal mechanism solutions, we suggested that the Mw 5.0 Qiaojia earthquake is more likely a tectonic earthquake. However, we cannot exclude the possibility that earthquakes could be induced after the impoundment of Baihetan Reservoir, because the identified concealed fault is located in the middle of many large fault zones and only 20 km away from the Baihetan Reservoir.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".