Adjoint Tomography of Ambient Noise Data and Teleseismic P Waves: Methodology and Applications to Central California
Bibliographic record
Abstract
Abstract Adjoint tomography has been recently applied to ambient seismic noise and teleseismic P waves separately to unveil fine‐scale lithospheric structures beyond the resolving ability of traditional ray‐based traveltime tomography. In this study, we propose an inversion scheme that alternates between frequency‐dependent traveltime inversions of ambient noise surface waves and waveform inversions of teleseismic P waves to take advantage of their complementary sensitivities to the Earth's structure. We apply our method to ambient noise empirical Green's functions from 60 virtual sources, direct P and scattered waves from 11 teleseismic events recorded by a dense linear array (∼7 km station spacing) and other regional stations (∼40 km average station spacing) in central California. To evaluate the performance of the method, we compare tomographic results from ambient noise adjoint tomography, full‐waveform inversion of teleseismic P waves, and the alternating inversion of the two data sets. Both applications to practical field data sets and synthetic checkerboard tests demonstrate the advantage of the alternating inversion over individual inversions as it combines the complementary sensitivities of the two independent data sets toward a more unified model. The three dimensional model from our alternating inversion not only shows major features of velocity anomalies and discontinuities in agreement with previous studies, but also reveals small‐scale heterogeneities which provide new constraints on the geometry of the Isabella Anomaly and mantle dynamic processes in central California. The proposed alternating inversion scheme can be applied to other regions with similar array deployments for high‐resolution lithospheric imaging.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".