Shear wave seismic interferometry for lithospheric imaging: Application to southern Mexico
Bibliographic record
Abstract
Abstract Seismic interferometry allows for the creation of new seismic traces by cross correlating existing ones. With sufficient sampling of remote‐source positions, it is possible to create a virtual source record by transforming a receiver location into a virtual source. The imaging technique developed here directly retrieves reflectivity information from the subsurface. Other techniques, namely receiver‐function and tomography, rely on mode‐converted energy and perturbations in a velocity field, respectively, to make inferences regarding structure. We select shear phases as an imaging source because of their lower propagation velocity, sensitivity to melt, and ability to treat vertical shear and horizontal shear wavefields independently. Teleseismic shear phases approximate a plane wave due to the extent of wavefront spread compared to a finite receiver array located on the free surface. The teleseismic shear phase transmission responses are used as input to the seismic interferometry technique. We create virtual shear source records by converting each receiver in the array into a virtual source. By cross correlating the received signals, the complex source character of distant earthquakes is imprinted on the virtual source records as the average autocorrelation of individual source‐time functions. We demonstrate a technique that largely removes this imprint by filtering in the common‐offset domain. A field data set was selected from the Meso‐America Subduction Experiment. Despite the suboptimal remote‐source sampling, an image of the lithosphere was produced that confirms features of the subduction zone that were previously found with the receiver‐function technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 0.001 |
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 teacher head, 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".