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Record W6910381446 · doi:10.4231/d3cj87m40

The Alternative Characteristic (AC) Model as Implemented for the BC Hydro SSHAC 3 Seismic Source Model

2014· article· en· W6910381446 on OpenAlexaboutno aff

Bibliographic record

VenueTexas Advanced Computing Center · 2014
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMagnitude (astronomy)Source modelRange (aeronautics)Margin (machine learning)Ground motionExponential functionEvent (particle physics)Earthquake prediction

Abstract

fetched live from OpenAlex

Magnitude-frequency recurrence models have long been a subject of seismological interest, and are a critical component of PSHA, since earthquake rates determine ground motion exceedance rates. While varieties of the Gutenberg-Richter (GR) truncated exponential recurrence model have long been verified and applied in many cases, it has been recognized that rates of larger earthquakes, based on historical occurrence and/or paleoseismic studies, sometimes show higher rates than the GR model predicts from smaller-magnitude events. A “characteristic” recurrence model accounting for such observations was published in 1985 by Youngs and Coppersmith, where “characteristic” refers to the larger earthquakes occurring at a higher-than-GR rate. This model is commonly used as an alternative to the GR model in PSHA studies, and is strictly defined: an exponential portion for smaller magnitude events, and a single rate for “characteristic” larger-magnitude events over a range of 0.5 magnitude unit that is dependent on properties of the exponential portion. Examination of observed earthquake recurrence in the areal zones of the BC Hydro PSHA seismic source model showed that several zones exhibited patterns similar to the 1985 characteristic model. These zones were the Puget Lowlands, Intermountain Seismic Belt, Olympic Peninsula and Southern Coastal Margin (predominantly Vancouver Island). Attempts to fit the observed earthquake recurrence to the standard 1985 characteristic model were unsuccessful. Therefore an effort to qualitatively fit these data to a functional form similar to the incremental form of the 1985 model was made by experimenting with different functional forms for the “characteristic” part of the 1985 model. The best fit was found by changing the slope of the characteristic part from zero (flat) to 0.3, and increasing its width from 0.5 to 1.0 magnitude units. An additional parameter allows the fraction of the total moment rate in the exponential and characteristic parts of the curve to be specified. For the four areal zones examined, the fraction assigned to the characteristic part ranged from 0.94 to 0.98. For use in the BC Hydro PSHA an average value of 0.96 was used. Termed the alternative characteristic (AC) model, it was applied to each areal zone in the seismic source model, with a weight influenced by whether paleoseismic information already existed to more reliably constrain the rates of large-magnitude events, or whether rates of historic seismicity tended to be approximately consistent with it. If these bases did not exist, it was weighted equally with the GR model.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.011
GPT teacher head0.251
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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