Transmission to reflection transformation of teleseismic wavefields
Bibliographic record
Abstract
In this paper we review the transformation of P and SV transmission Green's functions into the corresponding reflection quantities for one‐dimensional, elastic media at precritical slownesses. To obtain estimates of the transmission Green's functions from observed data, we apply a recently developed approach that exploits the minimum‐phase nature of the direct waves and employs autospectra and cross‐spectra of raw component seismograms representing multiple sources recorded at a single station. To accomplish transformation to reflection Green's functions, we outline a practical recipe that involves amplitude balancing, energy normalization, cross correlation, and removal of free surface effects. We assess its performance through application to both synthetic seismograms and data from station Hyderabad. In all cases we are able to identify the first‐order scattering contributions from the continental Moho. The transmission‐to‐reflection transformation has potential applications for imaging hitherto elusive shallow mantle discontinuities, since interfering forward and back scattering contributions are effectively separated by component in the resulting reflection Green's functions.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".