Waveform Modeling of the Crust and Upper Mantle Using S, Sp, SsPmP, and Shear-Coupled PL Waves
Bibliographic record
Abstract
We developed a waveform modeling code that computes synthetic seismograms with a parallelized reflectivity method and fits the observed waveforms by global optimization. Assuming a 1-D, isotropic, layered Earth, our code computes synthetic seismograms for all layers, frequencies, and ray parameters. It implements a global optimization algorithm using Very Fast Simulated Annealing that allows for broad model space search so as to rind the global minimum, and hence minimizes dependency on the starting model. Our method also computes the Posterior Probability Densities and correlation matrices to evaluate the uniqueness of the resulting models and trade-offs between individual model parameters. We applied the code to determine the crust and upper mantle structure beneath permanent broadband seismic stations in Africa, China, and Canada using large teleseismic earthquakes recorded at these stations. We modeled the S, Sp, SsPmP, and shear-coupled PB waves from these earthquakes and our P- and S-wave velocity models compare well with, and in some cases improve upon the models obtained from other existing methods. Our use of the shear-coupled PB phase wherever available improved constraints on the models of the lower crust and upper mantle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".