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Record W4402545656 · doi:10.36487/acg_repo/2465_61

Seismic analysis of abutment events at LaRonde mine

2024· article· en· W4402545656 on OpenAlexaboutno aff
Benjamin Ollila

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsAbutmentGeologyMining engineeringGeotechnical engineeringSeismologyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The purpose of this paper is to characterise the source mechanisms of two large seismic events (magnitude Nuttli ≥ 3.0) using routine seismic data analysis tools. When there is no clear evidence of source mechanism type, there is a prevailing tendency to attribute fault slip mechanisms to large magnitude seismic events. This study uses seismic source parameter analysis to highlight characteristics of two large events that are more consistent with a stress-driven mechanism than a fault-related failure process. While fault-related seismicity tends to be confined to the plane of a geologic feature, stress-driven seismicity tends to be controlled by regions of mining-induced stress around mine voids and can migrate as mining progresses. Using seismic data from an ultra-deep open stoping mine in northern Quebec, this study characterises a migrating rock mass failure region in the mine abutments. The locations of seismic events, including mine-scale occurrences, are linked to the advancing stoping front of the mine abutment. Introducing a novel tool, plane-based time–distance charts, enables the exploration of migrating regions of rock mass yield and facilitates event clustering for source parameter analysis. The self-similarity of the large events with the broader migrating failure region is assessed using the Gutenburg–Richter frequency–magnitude relation. This analysis sheds light on the distinctive nature of stress-induced rock mass yield zones, providing insights for seismic hazard assessment in deep mining environments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.197
Teacher spread0.191 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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