3‐D Magnetotelluric Imaging of the Easternmost Kunlun Fault: Insights Into Strain Partitioning and the Seismotectonics of the Jiuzhaigou Ms7.0 Earthquake
Bibliographic record
Abstract
Abstract We present a three‐dimensional electrical resistivity model of the crust and upper mantle beneath the easternmost Kunlun fault (EKLf), obtained by three‐dimensional inversion of magnetotelluric (MT) data. The crust of the Songpan‐Ganzi block is characterized by high resistivity from the surface to a depth of around 20 km, and by low resistivity in the mid‐lower crust in the depth range 20–40 km. The eastern edge of the high conductivity layer is coincident with the EKLf and the Huya fault. The electrical resistivity structure provides new insights into both (1) the generation of recent M > 6 earthquakes and (2) strain partitioning on this segment of the EKLf. Our model reveals that the Huya fault is the main branch of the EKLf in the region. Together with the EKLf, the Huya fault defines the boundary between the Songpan‐Ganzi and Bikou blocks. The mid‐lower crust of the Songpan‐Ganzi block in this region has a low resistivity that likely represents a mechanically weak layer. The 2017 Jiuzhaigou Ms7.0 earthquake and other recent M > 6 earthquakes may have been controlled by the change in viscosity in the mid‐lower crust that occurs across this boundary. The high conductivity may be acting as either (1) a channel of lower crustal flow, or (2) as a weak layer that decouples the upper and lower crust. The fact that the high conductivity layer does not extend along the north side of the Sichuan Basin questions the idea that crustal flow occurs in this area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".