The effects of discontinuity topography in the mantle transition zone on global geodynamic observables and mantle heterogeneity
Bibliographic record
Abstract
SUMMARY Despite progress in tomographic imaging of Earth's interior, a number of critical questions regarding the large-scale structure and dynamics of the mantle remain outstanding. One of those questions is the impact of phase-boundary undulations on global imaging of mantle heterogeneity and on geodynamic (i.e. convection-related) observables. To address this issue, we developed a joint seismic-geodynamic-mineral physical tomographic inversion procedure that incorporates lateral variations in the depths of the 410- and 660-km discontinuities. This inversion includes S-wave traveltimes, SS precursors that are sensitive to transition-zone topography, geodynamic observables/data (free-air gravity, dynamic surface topography, horizontal divergence of tectonic plates and excess core-mantle boundary ellipticity) and mineral physical constraints on thermal heterogeneity. Compared to joint tomography models that do not include data sensitivity to phase-boundary undulations in the transition zone, the inclusion of 410- and 660-km topography strongly influences the inference of volumetric anomalies in a depth interval that encompasses the transition zone and mid-mantle. It is notable that joint tomography inversions, which include constraints on transition-zone discontinuity topography by seismic and geodynamic data, yield more pronounced density anomalies associated with subduction zones and hotspots. We also find that the inclusion of 410- and 660-km topography may improve the fit to the geodynamic observables, depending on the weights applied to seismic and geodynamic data in the inversions. As a consequence, we find that the amplitude of non-thermal density anomalies required to explain the geodynamic data decreases in most of the mantle. These findings underline the sensitivity of the joint inversions to the inclusion of transition-zone complexity (e.g. phase-boundary topography) and the implications for the inferred non-thermal density anomalies in these depth regions. Finally, we underline that our inferences of 410- and 660-km topography avoid a commonly employed approximation that represents the contribution of volumetric heterogeneity to SS-wave precursor data. Our results suggest that this previously employed correction, based on a priori estimates of upper-mantle heterogeneity, might be a significant source of error in estimating the 410- and 660-km topography.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".