Investigation of seismic anisotropy in the D’’ layer and at the CMB underneath Siberia and the North Atlantic
Bibliographic record
Abstract
Within the Priority Program 2404 “Reconstructing the Deep Dynamics of Planet Earth over Geologic Time” (DeepDyn, https://www.geo.lmu.de/deepdyn/en/) we investigate possible seismic signatures at magnetic high-latitude flux lobes (HLFLs). The focus is on four target regions on the Northern Hemisphere: Siberia, Canada, the North Atlantic, and Indonesia. While Siberia and Canada show the HLFLs, the North Atlantic should be the location of a third postulated HLFL, but this area does not show an intense-flux signal in the magnetic field. The region beneath Indonesia and the Indian Ocean is characterized by an area of intense magnetic flux that changes direction and moves westwards over time. Our aim is to understand whether mineralogy and seismic structure (i.e., thermal constraints) could be responsible for the different magnetic signatures at the core mantle boundary (CMB). This is done by combining two approaches: seismic anisotropy (KIT) and seismic reflections (University of Münster) near the CMB (https://www.geo.lmu.de/deepdyn/en/projects/ritter-joachim-und-thomas-christine-understanding-the-influence-of-deep/).To study anisotropy, we measure shear wave splitting (SWS) of SKS, SKKS, and PKS phases. Thereby, we determine the splitting parameters, the fast polarization direction φ and the delay time δt, using both the energy-minimization and the rotation-correlation methods. Especially, we search for phase pair discrepancies based on the observation type (null vs. split), e.g., between SKS and SKKS phases, as they are a clear indication for a lowermost mantle contribution to the splitting signal. For the target region underneath Siberia, SWS measurements are obtained using earthquakes with epicenters in Southeast Asia recorded at stations in the North of Scandinavia and Svalbard as well as earthquakes with epicenters in Central America recorded at the station ULN in Mongolia. These SWS measurements indicate that for the discrepant pairs the phases with piercing points closer to the center of the HLFL beneath Siberia show splitting while the phases more distant to the HLFL do not show anisotropy. Furthermore, we present first results for the target region North Atlantic. Based on our SWS measurements, we will derive structural and mineralogical anisotropy models using the MATLAB Seismic Anisotropy Toolbox (Walker and Wookey 2012). To test these models, we then simulate synthetic seismograms using AxiSEM3D (Leng et al. 2016, 2019).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".