High‐Resolution Mantle Transition Zone Imaging Using Multi‐Dimensional Reconstruction of SS Precursors
Bibliographic record
Abstract
Abstract The SS precursors have been extensively utilized in mapping the mantle transition zone (MTZ). However, their applications are often challenged by weak phases that arise from small impedance contrasts of the mantle discontinuities, noise contamination, and localized thermal/compositional heterogeneities. We develop a new data processing workflow for more reliable MTZ imaging by adopting the recently proposed robust damped rank‐reduction (RDRR) method from exploration seismology. This method exploits the signal coherency in the four‐dimensional data and allows simultaneously attenuating noise and interpolating missing traces. We utilize synthetic data sets generated with realistic earth structures and MTZ topography to evaluate the capability of the proposed workflow. Our test results show that the RDRR method can well capture the topographic variation of mantle discontinuities, improving the SNR of the SS precursor data by an order of magnitude. The application to SS precursors from the western Pacific successfully removes contaminating noises, mitigates imaging artifacts of small‐scale anomalies, and improves the lateral coherency of the MTZ structure, revealing a clear first‐order structural transition. Compared with earlier global and regional models, our model reveals more structural details including a localized thin MTZ near the Changbai volcano in East Asia. This observation, in conjunction with the reported low‐velocity structure in the MTZ in earlier tomographic studies, may support the presence of deep mantle up‐welling through a slab gap. In summary, our work enables resolving MTZ structures with high fidelity and highlights the importance of advanced array methods in improving SS precursor imaging.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".