Detecting lower-mantle slabs beneath Asia and the Aleutians
Bibliographic record
Abstract
To investigate the descend of subducted slabs we search for and analyse seismic arrivals that reflected off the surface of the slab. In order to distinguish between such arrivals and other seismic phases, we search for waves that reach a seismic array with a backazimuth deviating from the theoretical backazimuth of the earthquake. Source–receiver combinations are chosen in a way that their great circle paths do not intersect the slab region, hence the direct arrivals can serve as reference. We focus on the North and Northwest Pacific region by using earthquakes from Japan, the Philippines and the Hindu Kush area recorded at North American networks (e.g. USArray, Alaska and Canada). Using seismic array techniques for analysing the data and record information on slowness, backazimuth and traveltime of the observed out-of-plane arrivals we use these measurements to trace the wave back through a 1-D velocity model to its scattering/reflection location. We find a number of out-of-plane reflections. Assuming only single scattering, most out-of-plane signals have to travel as P-to-P phases and only a few as S-to-P phases, due to the length of the seismograms we processed. The located reflection points present a view of the 3-D structures within the mantle. In the upper mantle and the transition zone they correlate well with the edges of fast velocity regions in tomographic images. We also find reflection points in the mid- and lower mantle and their locations generally agree with fast velocities mapped by seismic tomography models suggesting that in the subduction regions we map, slabs enter the lower mantle. To validate our approach, we calculate and process synthetic seismograms for 3-D wave field propagation through a model containing a slab-like heterogeneity. We show, that depending on the source–receiver geometry relative to the reflection plane, it is indeed possible to observe and back-trace out-of-plane signals.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".