Panel deconvolution of receiver-function gathers: improved images via cross-trace constraints
Bibliographic record
Abstract
A method is presented for deconvolving collections of receiver functions at individual seismic stations. Conventional deconvolution trades-off signal enhancement (through stacking or simultaneous deconvolution) with preservation of directional variation. I replace stacking of traces with minimization of directional derivatives, thus communicating information between traces without stacking and recovering the minimum directional variation required by the data. Directional derivatives are defined by triangulation of the incident horizontal slownesses, computing derivatives at the centre of each triangle. Deconvolution is then posed as a simultaneous inversion for all traces, incorporating directional derivative minimization as cross-trace constraints. The constraints share frequency content between traces, while preserving directional variation. The resulting receiver function gathers have greater intertrace coherence, lower noise levels and higher usable frequencies than standard methods. A decade of teleseismic data at station GAC (Quebec, Canada) are deconvolved using conventional and panel deconvolution; the new technique produces a coherent receiver-function image and reveals complex crust and mantle responses. An extension of the method to recover harmonic coefficients of backazimuthal variation is also included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".