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Analysis of microseismic cluster locations based on the evolution of mining-induced stresses

2014· article· en· W2624292530 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDeep mining · 2014
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsMcGill UniversityVale (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroseismBoreholeInduced seismicityGeologyRange (aeronautics)Rock mass classificationMining engineeringSeismologyComputer scienceData miningGeotechnical engineeringEngineering

Abstract

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Numerical modelling is increasingly being used in the mining industry as part of the planning process. Its areas of application range from the estimation of in situ stresses at planned locations of underground facilities, to the effects of stope sequence alternatives on drift instability. In terms of the size of their study area, numerical models can range from a section of a given level to mine-wide dimensions, with an increase in complexity and input information requirements. Microseismic activities induced by mining operations can be studied using mine-wide numerical models that have been properly calibrated. In this paper, mining-induced seismicity at the Vale Garson Mine is examined between 2006 and 2008 with a numerical model constructed in FLAC3D. Two sets of microseismic activities are used as a basis of the study; events from the microseismic database with energy outputs greater than 100 kJ, and events that have resulted in rockbursts within developments, regardless of their energy outputs, for a total of 24 events. In the first phase of the study, the coordinates and location error of each event, as obtained from the microseismic database, are used to construct a location cube defining the maximum boundaries within which the actual coordinates must lie. Based on the 24 location cubes plotted, four microseismic clusters are identified. In the second phase, the mine-wide model is calibrated based on laboratory results of rock samples, borehole data of rock mass properties, and an in situ stress measurement point on 4900L (1,495 m). The historical stope sequence followed at the mine is replicated in the model from 2001 to 2008. Mining-induced stresses within the location cubes of two clusters are examined using the maximum shear stress, brittle shear ratio, and the continuous change in differential stress (CC-DS) when compared to pre-mining conditions. It is shown that all event location cubes studied register an abrupt increase in CC-DS some time before or during the occurrence of that event. In the final phase, the most microseismically active zone in one of the geological units is compared to relatively quiet zones in terms of CC-DS conditions. It is shown that the CC-DS values are mostly constant in the latter zone, while they typically undergo abrupt and sudden changes in the active one prior to microseismic events. Hence, a new method of analysis with the potential of predicting the location of microseismic clusters is introduced.

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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: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.278

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 communication0.0000.000
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 head0.013
GPT teacher head0.215
Teacher spread0.203 · 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