Shear-Wave Anisotropy Measurements in the Crust from Receiver Functions: An Interplay of Lower and Upper Crustal Anisotropy
Bibliographic record
Abstract
We report a study using teleseismic P-wave receiver functions to infer the orientation of the maximum horizontal principal stress from the direction of upper crustal shear-wave velocity anisotropy. We apply an inverse approach using the Neighborhood Algorithm to conduct a nonlinear search, attaining a best-fitting crustal model that includes shear velocity anisotropy. Unlike previous methods reported in the literature, this method is able to distinguish anisotropy in the upper, brittle crust from that in the lower, ductile crust in certain instances. We apply this method to teleseismically recorded earthquakes in the Central Valley of California, the Permian Basin, Texas, northern Oklahoma and sites near the San Andreas Fault in California. Of the forty-one stations to which we apply this method, twenty have a good apparent signal. A misfit calculation is performed by calculating a zero-lag cross-correlation coefficient for each modeled receiver function with the data for a given back azimuth range. While the fast polarization direction in the upper crust of some of these stations aligns with independent indicators of the direction of the maximum horizontal principal stress, the fast direction in the upper crust at other stations does not, apparently indicating that the anisotropy was resulting from a different mechanism.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".