Post-critical SsPmp and its Applications to Virtual Deep Seismic Sounding (VDSS) 2: 1D Imaging of the Crust/Mantle and Joint Constraints with Receiver Function
Bibliographic record
Abstract
Virtual Deep Seismic Sounding (VDSS) has emerged as a novel method to image the crust-mantle-boundary (CMB) and potentially other lithospheric boundaries. In Part 1 (Liu et al., 2018), we showed that the arrival time and waveform of post-critical SsPmp, the post-critical reflection phase at the CMB used in VDSS, is sensitive to several different attributes of the crust and upper mantle. Here, we synthesize our methodology of deriving Moho depth, average crustal Vp and uppermost-mantle Vp from single-station observations of post-critical SsPmp under a 1D assumption. We first verify our method with synthetics and then substantiate it with a case study using the Yellowknife and POLARIS arrays in the Slave Craton, Canada. We show good agreement of crustal and upper-mantle properties derived with VDSS with those given by previous active-source experiments and our own P receiver functions (PRF) in our study area. Finally, we propose a PRF-VDSS joint analysis method to constrain average crustal Vp/Vs ratio and composition. Our PRF-VDSS joint analysis shows that the southwest Slave Craton has an intermediate crustal composition, most consistent with a Mesoarchean age.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".