Lithospheric imaging via teleseismic scattering tomography
Bibliographic record
Abstract
The coda of the teleseismic P phase consists largely of energy scattered by small inhomogeneities in the receiver-side lithosphere. Given large collections of teleseismic data from dense permanent networks, previous workers have successfully back-propagated coda energy back to scattering source points using various kinematic migration schemes, as well as by inverting using an inverse scattering/radon transform approach. Under the Born approximation, seismic scattering is a linear process; therefore it is possible to approach coda scattering as a linear waveform inversion problem, mathematically similar to transmission-based tomography. Assuming ray-theoretical propagation and Rayleigh scattering, we pose the inverse scattering problem in tomographic form, and recover perturbations in density and P and S velocities from Pp and Ps scattered data. The method is applied to data from the Southern California Seismic Network (SCSN) covering the San Jacinto–Anza region. The results show a considerable correlation between seismicity and velocity perturbation structure, particularly in the region between the Mission Creek and Banning fault branches. Features connecting the Coyote Creek and Elsinore faults at right angles are correlated with seismicity lineations and may represent conjugate faulting with no surface expression.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".