High-resolution seismic array imaging based on an SEM-FK hybrid method
Bibliographic record
Abstract
We demonstrate the feasibility of high-resolution seismic array imaging based on teleseismic recordings using full numerical wave simulations. We develop a hybrid method that interfaces a frequency–wavenumber (FK) calculation, which provides analytical solutions to 1-D layered background models with a spectral-element (SEM) numerical solver to calculate synthetic responses of local media to plane-wave incidence.This hybrid method accurately deals with local heterogeneities and discontinuity undulations, and represents an efficient tool for the forward modelling of teleseismic coda (including converted and scattered) waves. We benchmark the accuracy of the SEM-FK hybrid method against FK solutions for 1-D media. We then compute sensitivity kernels for teleseismic coda waves by interacting the forward teleseismic waves with an adjoint wavefield, produced by injecting coda waves as adjoint sources, based on adjoint techniques. These sensitivity kernels provide the basis for mapping variations in subsurface discontinuities, density and velocity structures through non-linear conjugate-gradient methods. We illustrate various synthetic imaging experiments, including discontinuity characterization, volumetric structural inversion for the crust or subduction zones. These tests show that using pre-conditioners based upon the scaled product of sensitivity kernels for different phases, combining finite-frequency traveltime and waveform inversion, and/or adopting hierarchical inversions from long- to short-period waveforms could reduce the non-linearity of the seismic inverse problem and speed up its convergence. The encouraging results of these synthetic examples suggest that inversion of teleseismic coda phases based on the SEM-FK hybrid method and adjoint techniques is a promising tool for structural imaging beneath dense seismic arrays.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".