Approaches to model non-uniqueness and site complexity for non-invasive shear-wave depth profiling
Bibliographic record
Abstract
Shear-wave velocity (VS) depth profiling and associated seismic site classifications were performed at 15 sites across Metro Vancouver, British Columbia using passive seismic and surface wave methods. Inversion model parameters are constrained at each site using nearby geodata in combination with developing regression models of shear-wave velocity with depth for three primary stratigraphic units. Statistical methods such as a Bayesian Information Criterion are applied post-inversion to evaluate models between and within varying parameterizations. Data evaluation metrics, including the use of microtremor horizontal-to-vertical spectral ratios (MHVSRs), are applied to identify two common deviations from the simple case of normally dispersive laterally homogeneous soils typically associated with surface-wave methods: lateral variations and velocity inversions. Lateral site variability is overcome by using the spatial variability in MHVSR peak frequency to sub-divide the site into quadrants for which quadrant-specific dispersion curve inversion provides reliable site classification for each quadrant. Velocity inversions are captured by performing inversions using partial fundamental mode dispersion curves. Partial inclusion of apparent-mode dispersion estimates is a reasonable compromise to modelling velocity inversions, providing a site classification between that of removing the apparent mode estimates (minimum velocity inversion modelling) and wrongfully treating apparent mode estimates as the fundamental mode (maximum velocity inversion modelling). These accessible approaches overcoming lateral site variability and apparent-mode dispersion estimates related to velocity inversions are proposed to obtain reliable seismic site classifications.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".