Migration Imaging of the Java Subduction Zones
Bibliographic record
Abstract
Abstract Imaging of tectonically complex regions can greatly benefit from dense network data and resolution enhancement techniques. Conventional methods in the analysis of SS precursors stack the waveforms to obtain an average discontinuity depth, but smearing due to large Fresnel zones can degrade the fine‐scale topography on the discontinuity. To provide a partial solution, we introduce a depth migration algorithm based on the common scattering point method while considering nonspecular diffractions from mantle transition zone discontinuities. Our analysis indicates that, beneath the Sunda arc, the depth of the 410 km discontinuity (the 410) is elevated by 30 km and the 660 km discontinuity (the 660) is depressed by 20–40 km; the region of the strongest anticorrelation is correlated with the morphology of the subducting Indo‐Australian slab. In eastern Java, a “flat” 410 coincides with a documented slab gap, showing length scales greater than 400 km laterally and 200 km vertically. This observation could be explained by the arrival of a buoyant oceanic plateau at the Java trench at approximately 8 Ma ago, which may have caused a temporary cessation of subduction and formed a tear in the subducting slab. Our results highlight contrasting depths of the 410 and 660 along the shallow‐dipping slab below the Banda trench. The 660, however, becomes significantly uplifted beneath the Banda Sea, which is accompanied by enhanced reflection amplitudes. We interpret these observations as evidence for a subslab low‐velocity zone, possibly related to the lower mantle upwelling beneath the subducting slab.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".