3D Shear‐Wave Velocity and Density Modeling of the Northern Cascadia Subduction Zone
Bibliographic record
Abstract
Abstract In the Northern Cascadia Subduction Zone (NCSZ), we developed a new 3D model for shear‐wave velocity (VS) and density to improve seismic hazard assessments and ground motion simulations. Utilizing seismic data from the past two decades, we measure surface wave dispersion from ambient noise and earthquake recordings and inverted them for VS perturbations at various depths. Additionally, Bouguer anomaly data were inverted for a 3D density distribution. These models provide direct constraints on shear‐wave velocities and density properties across the study region. Our findings corroborate previous research, highlighting: (a) anomalous low velocity zones in the Puget Lowland, attributed to subduction dehydration and trapped fluid‐rich sediments; (b) low VS beneath sedimentary basins, delineating their geometry; (c) deep‐seated crustal faults on Vancouver Island indicated by segmented high‐VS zones; (d) a high‐velocity anomaly corresponding to the subducting oceanic slab beneath the Olympic Peninsula beneath characterized by an anomalously slow upper mantle velocity; and (e) localized high‐velocity layers straddled between low‐velocity layers in the upper crust associated with magmatic processes. A novel aspect of our work is the identification of a high‐density anomaly rising from the uppermost mantle, aligning with the Puget Sound waterway trajectory. This anomaly, detected at depths exceeding 20 km, provides new insights into the dynamics of oceanic crust‐mantle coupling beneath the study area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".