Crust and Upper Mantle Structure of the South China Sea and Adjacent Areas From the Joint Inversion of Ambient Noise and Earthquake Surface Wave Dispersions
Bibliographic record
Abstract
Abstract In this study, we built a high‐resolution 3D S‐wave velocity model of the South China Sea (SCS) and adjacent areas by the joint inversion of surface wave dispersions retrieved from ambient noise and earthquake data. We first measured Rayleigh wave phase velocity dispersions from the waveforms recorded by 94 broadband seismic stations. Subsequently, we used a generalized least squares inversion scheme with spatially varying resolution to perform 2D tomographic inversion at different periods. Lastly, we performed 1D inversion at different grid points and obtained the 3D S‐wave velocity model in the depth range of 15–250 km. Our model reveals a prominent high velocity (high‐V) anomaly beneath the SCS basin and shows that the lithosphere beneath the SCS basin is about 70–80 km thick. We observe a prominent low velocity (low‐V) anomaly in the asthenosphere beneath the SCS basin, and we interpret this as evidence for the ongoing partial melting of the asthenosphere. An obvious high‐V anomaly is also revealed beneath the Khorat Plateau (KP) in the depth range of 80–150 km, suggesting that the KP has a cold and thick lithosphere and has suffered limited destruction in the Cenozoic era. In the northern part of Kalimantan Island (KI), a prominent low‐V anomaly is shown in the upper 60 km of the lithosphere revealing the upwelling of the asthenosphere. Our findings shed lights on the tectonic evolution of the SCS and adjacent areas.
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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 | 0.000 | 0.000 |
| 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".