Moho Inversion of East China Sea and Its Adjacent Areas Based on Potential Field Data
Bibliographic record
Abstract
East China Sea and its adjacent areas are an important part of the circum-Pacific tectonic belt, and its fluctuation characteristics of Moho can provide an important basis for the study of the deep structure of the western Pacific. At present, the inversion of Moho depth based on potential field data is an important task. Meanwhile, its inversion accuracy is closely related to the gravity anomaly data quality, and to the density contrasts and inversion algorithm. We use the fast solution algorithm of forward problem for gravity field in a dual interface model to eliminate the gravity influence of terrain and sediments, and adopt the minimum curvature potential field separation method to remove the effect of residual geological bodies. Then we try to identify the Moho gravity anomaly as the regional field which has the strongest correlation with depths estimations from seismic data. Regression analysis and the "3σ" principle are used to delete the constraint points of Moho depth with large deviations, and the Bouguer plate formula is used to estimate the laterally variable density contrasts of Moho. Finally, the Moho depth in East China Sea and its adjacent areas is obtained by the dual-interface fast inversion algorithm, and the inversion deviations are mostly concentrated within 2 km. The inversion result shows that there is an obvious local uplift zone of Moho in East China Sea Basin, while the Okinawa Trough basin is located on a whole Moho uplift. The strike of the two uplift belts has the same change from NE to NNE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".