Singular spectrum analysis and its applications in mapping mantle seismic structure
Bibliographic record
Abstract
Seismic discontinuities are fundamental to the understanding of mantle composition and dynamics. Their depths and impedance contrasts are generally determined using secondary phases such as SS precursors and P-to-S converted waves. However, analysing and interpreting these weak signals often suffer from incomplete data coverage, high noise levels and interfering seismic arrivals, especially near tectonically complex regions such as subduction zones. To overcome these pitfalls, we adopt a singular spectrum analysis (SSA) method to remove random noise, reconstruct missing traces and enhance the robustness of SS precursors and P-to-S conversions from mantle seismic discontinuities. Our method takes advantage of the predictability of time series in the frequency–space domain and performs rank reduction using a singular value decomposition of the trajectory matrix. We apply SSA to synthetic record sections as well as the observations of (1) SS precursors beneath the northwestern Pacific subduction zones, and (2) P-to-S converted waves from southwestern Canada. In comparison with raw or interpolated data, the SSA enhanced seismic sections exhibit greater resolution due to the suppression of random noise (which reduces signal amplitude during standard averaging procedures) through rank reduction. SSA also enables an effective separation of the SS precursors from the postcursors of S-wave core diffractions. This method will greatly benefit future analyses of weak crustal and mantle seismic phases, especially when data coverages are less than ideal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".