Receiver functions using OBS data: promises and limitations from numerical modelling and examples from the Cascadia Initiative
Bibliographic record
Abstract
The expanding fleet of broad-band ocean-bottom seismograph (OBS) stations is facilitating the study of the structure and seismicity of oceanic plates at regional scales. For continental studies, an important tool to characterize continental crust and mantle structure is the analysis of teleseismic P receiver functions. In the oceans, however, receiver functions potentially suffer from several limiting factors that are unique to ocean sites and plate structures. In this study, we model receiver functions for a variety of oceanic lithospheric structures to investigate the possibilities and limitations of receiver functions using OBS data. Several potentially contaminating effects are examined, including pressure reverberations from the water column for various ocean-floor depths and the effects of a layer of low-velocity marine sediments. These modelling results indicate that receiver functions from OBS data are difficult to interpret in the presence of marine sediments, but shallow-water sites in subduction zone forearcs may be suitable for constraining various crustal elements around the locked megathrust fault. We propose using a complementary approach based on transfer function modelling combined with a grid search approach that bypasses receiver functions altogether and estimates model properties directly from minimally processed waveforms. Using real data examples from the Cascadia Initiative, we show how receiver and transfer functions can be used to infer seismic properties of the oceanic plate in both shallow (Cascadia forearc) and deep (Juan de Fuca Ridge) ocean settings.
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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 | 0.000 | 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) | 0.000 | 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 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".