Layer stripping the response from sedimentary basins in teleseismic data using transfer functions
Bibliographic record
Abstract
Receiver functions generated from distant earthquakes have long been used to determine deep crustal and mantle structure beneath seismic stations. Teleseismic research over sedimentary basins often has to contend with cluttering of the seismic response by basin reverberations. A sedimentary basin typically exhibits a sharp velocity contrast at their base, which acts as a strong reflector for downgoing seismic waves, leading to reverberations within the basin between the basement interface and freesurface. These reverberations obscure important P to S converted arrivals and Moho reverberations in receiver functions. This research tests whether it is possible to remove the effects caused by a sedimentary basin (layer-strip) using imperfect velocity models. A filter is generated using the propagator method, as the spectral ratio of two model responses: one containing the basin and one without. The transfer function, generated as the ratio between the two responses, acts as an acausal filter. Tests on synthetic data were used to evaluate the level of model accuracy required for effective filtering. The method was then tested on real data from Canadian National Seismograph Network station EDM (Edmonton, Alberta, in the Western Canada Sedimentary basin) as well as a line of EarthScope Transportable Array stations over the Williston Basin. Results indicate that the effectiveness of the filter is correlated with the quality of the model, and that the method significantly improves the recovery of Moho reverberations (and thus the quality of an H-k stack) on real teleseismic data collected over sedimentary basins. As a quality basin model is required to achieve trustworthy filtering, the layer-strip methodology is particularly useful in areas where a velocity model is produced with two or more lines of evidence.
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 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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".