Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography
Bibliographic record
Abstract
Abstract Adjoint tomography has recently been applied to ambient noise data as a new and promising tomographic method that utilizes simulation‐based 3‐D sensitivity kernels rather than ray theory used in traditional ambient noise tomography. However, to date, most studies of ambient noise adjoint tomography only use vertical‐component Rayleigh waves. In this study, we develop a theoretical framework for calculating sensitivity kernels for multicomponent empirical Green's functions extracted from ambient noise data. Under the framework of the adjoint method, we demonstrate that a horizontal component (transverse‐transverse or radial‐radial) kernel can be constructed from the interaction of wave fields generated by point‐force sources acting in the north and east directions based on rotation relationships. Our method is benchmarked for a 3‐D heterogeneous isotropic model by comparing rotated seismograms, individual, and event traveltime misfit kernels with corresponding references computed by numerical simulations with sources directly placed in the radial or transverse directions. Based on our new method, we perform the first Love‐wave ambient noise adjoint tomography in southern California and construct an improved VSH model. Our method for computing sensitivity kernels of multicomponent empirical Green's functions provides the basis for multicomponent ambient noise adjoint tomography in imaging radially anisotropic shear‐wave velocity structures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".