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Record W2326079466 · doi:10.3997/2214-4609.201413245

Seismic Hazard and Hydraulic Fracture-induced Seismicity

2015· article· en· W2326079466 on OpenAlexaff
G. Viegas, T. Urbancic, A. M. Baig

Bibliographic record

VenueProceedings · 2015
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsCanadian Apheresis Group
Fundersnot available
KeywordsInduced seismicitySeismologySeismic hazardMagnitude (astronomy)GeologyPeak ground accelerationReturn periodGround motionRange (aeronautics)EngineeringPhysicsGeography

Abstract

fetched live from OpenAlex

Summary Traditionally, to relate the seismic hazard potential in seismically active areas, empirical ground motion prediction equations (EGMPE) are used to relate event parameters like magnitude and location to site characteristics such as peak ground acceleration (PGA) or peak ground velocity (PGV) which tend to be how building codes are parametrized. There are some key differences between induced and natural seismicity – induced earthquakes tend to be shallower and to release less stress – which affect peak ground motions. Standard EGMPE derived from natural earthquakes may not be appropriatly applied to predict the ground motion generated by induced earthqakes. In this study we develop a new EGMPE specific for hydraulic fracture stimulations in the Horn River basin using PGV and PGA measurements resulting from Mw0.2 to Mw2.9 induced seismic events locally recorded over a two-year period. The strongest recorded PGA of 0.017g was obtained for the largest magnitude event in the dataset and corresponds to the lowest PGA range introduced in the 2014 USGS seismic hazard map for 2475 year return period and could only be felt by a few people. The EGMPE developed in this study can be extrapolated for similar larger magnitude events and included into future Probabilistic Seismic Hazard Analysis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.249
Teacher spread0.218 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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