Comparing felt intensity patterns for deep intraslab earthquakes in the Cascadia and Chilean subduction zones, offshore British Columbia, United States, and Chile
Bibliographic record
Abstract
In this study, we utilize US Geological Survey citizen science earthquake felt intensity data to investigate whether deep, intraslab earthquakes in the Chilean Subduction Zone show similar, 'felt intensity' distributions to events of the same magnitude and depths within the Cascadia Subduction Zone (Quitoriano & Wald, 2020; USGS Earthquake Hazards Program, 2020). In a companion article (Rutherford & Cassidy, 2022) we examine crustal earthquake intensity patterns for the Chile and Cascadia subduction zones. One goal of this comparison is to determine whether felt intensity information from several recent large (M8-8.8) subduction earthquakes in Chile can be applied to Cascadia (where no subduction earthquakes have been felt since 1700). This will provide a better understanding of shaking intensity patterns for future subduction earthquakes in Cascadia - critical information for scientists, engineers, and emergency management organizations. For this research, we utilized 20 years of catalogued 'Did You Feel It?' (DYFI) citizen science data from the US Geological Survey's (USGS) earthquake online catalogue, the ANSS Comprehensive Earthquake Catalog (ComCat) Documentation (USGS Earthquake Hazards Program, 2021). In total, we compared intensity patterns from five earthquakes in Cascadia (M4.8, M5.0. M6.7 & M6.8) to the intensity patterns from 157 earthquakes in Chile, with the same magnitudes as the Cascadia events (M4.8-M6.8). Our analysis involved plotting and fitting the Chile and Cascadia earthquakes' DYFI responses in order to compare the intensity patterns for the two subduction zones. Overall, we find good agreement between felt patterns in Chile and Cascadia. For example, all plots show the expected downward trend for intensity with distance; and there is generally a similar intensity clustering of responses around 50 to 300 km hypo-central distance. These results provide confidence that we can use Chilean intensity data for megathrust earthquakes in Cascadia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".