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Record W2089265865 · doi:10.1139/cgj-2014-0375

Evaluation of fault-slip potential due to shearing of fault asperities

2015· article· en· W2089265865 on OpenAlexafffundvenue
Atsushi Sainoki, Hani S. Mitri

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlip (aerodynamics)Seismic momentGeologyShearing (physics)Fault (geology)Shear (geology)SeismologyShear stressAsperity (geotechnical engineering)Geotechnical engineeringMining engineeringEngineeringPetrologyMechanics

Abstract

fetched live from OpenAlex

Mining activities in underground mines could induce fault-slip that inflicts devastating damage to mine openings. Thus, enhancing current knowledge about fault-slip taking place in underground mines is of paramount importance. In this study, static and dynamic analyses are performed using mine-wide models encompassing a fault running parallel to a steeply dipping, tabular orebody. In the static analysis, modelled stopes within the orebody are extracted and backfilled in accordance with a sublevel stoping method. Dynamic analyses may be performed, depending on the stress states at the end of each mining stage during the static analysis, to simulate fault-slip induced by a stress drop resulting from asperity shear. Selected seismic source parameters of the simulated fault-slip are then computed for each mining stage. The relation between D/H (where D is distance between the fault and the orebody, and H is height of the mined-out ore) and seismic source parameters is examined. It is shown that seismic moment and radiated seismic energy correlate well with D/H, thus suggesting that this ratio could be used as an indicator of fault-slip potential. On the other hand, no noticeable correlation between the maximum slip rate during fault-slip and D/H could be ascertained.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.038
GPT teacher head0.253
Teacher spread0.214 · 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 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

Citations33
Published2015
Admission routes3
Has abstractyes

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