An Engineering Approach to Seismic Risk Management in Hardrock Mines

Hudyma, Marty; Potvin, Yves

doi:10.1007/s00603-009-0070-0

Cited by 113 publications

(51 citation statements)

References 5 publications

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“…With the rapid development and application of MS monitoring techniques, many achievements have been made regarding our understanding of rock slopes (Lynch et al, 2005;Xu et al, 2011bXu et al, , 2012aXu et al, , 2014a, underground mining (Ge, 2005;Wang and Ge, 2008;Lesniak and Isakow, 2009;Kaiser et al, 2009;Trifu and Shumila, 2010;Young et al, 2004;Liu et al, 2013;Hudyma and Potvin, 2010;Lu et al, 2013), deep tunnels (Cai et al, 2001;Tang et al, 2011;Hirata et al, 2007;Feng et al, 2012) and the generation of electricity by hot dry rocks (Tezuka and Niitsuma, 2000). For example, Lynch et al (2005) investigated the engineering application of MS monitoring technique in two case studies and showed that the surface movements inferred from the MS data were spatially correlated with the actual surface movements.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Microseismic monitoring and stability evaluation for the large scale underground caverns at the Houziyan hydropower station in Southwest China

Dai

et al. 2015

Engineering Geology

111

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Section: Accepted Manuscriptmentioning

confidence: 99%

Microseismic monitoring and stability evaluation for the large scale underground caverns at the Houziyan hydropower station in Southwest China

Dai

et al. 2015

Engineering Geology

111

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“…Hierarchical cluster has been applied for seismicity partitioning since the 1990s, and Davis and Frohlich [11], Frohlich and Davis [12], Wardlaw et al [13], Hudyma [14], Hudyma and Potvin [15], Hashemi and Mehdizadeh [16], and Hashemi and Karimi [17] selected the hierarchical clustering technique using single-link analysis/Ward's method to evaluate spatial and temporal properties of earthquake catalogues. However, the hierarchical cluster has a tendency to form seismic events into linear groups [12].…”

Section: State Of the Artmentioning

confidence: 99%

K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)

Shang

Morales‐Esteban

et al. 2017

Shock and Vibration

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Seismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment. In this paper, seismic event location ( , , ) and Euclidean distance were selected as the -Means cluster, the Gaussian mixture model (GMM), and the self-organizing maps (SOM) input features and cluster determination measurement, respectively, and 1516 seismic events ( > −1.5) obtained from the Yongshaba mine (China) were chosen for the cluster analysis. In addition, a Silhouette and Krzanowski-Lai-(KL-) combined S-KL index was proposed to obtain the possible optimum cluster number and to compare the cluster methods. Results show that the -Means cluster obtains the best cluster "quality" with higher S-KL indexes on the whole and meaningful clusters. Furthermore, the optimal number for detailed geological structure interpretation is confirmed as eleven clusters, and we found that two areas probably have faults or caves, and two faults may be falsely inferred by mine geologists. Seismic hazard assessment shows that C5 and C7 ( = 11) have a high mean moment magnitude ( ) and C1, C2, C3, and C4 ( = 11) have a relatively high , where special attention is needed when mining. In addition, C7 ( = 11) is the most shear-related area with a mean S-wave to P-wave energy ratio ( Es/Ep ) of 41.21. In conclusion, the -Means cluster provides an effective way for mine seismicity partitioning, geological structure interpretation, and seismic hazard assessment.

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“…The analysis of AE parameter series of rock failure and precursory information data mining of instability are the important methods of prewarning model establishment of rock mass instability or disasters. So far, the study of AE characteristics mainly focuses on occurrence frequency [16][17][18][19], energy [20][21][22][23], spatial distribution [24][25][26], source parameters [27][28][29][30], and modern mathematical analysis [31][32][33][34][35][36][37] of AE activity. Among them, AE occurrence frequency and energy characteristics analysis are the earliest analytical methods, and now are still the most important means of disaster prewarning analysis, in both situ AE monitoring without source location and microseismic (MS) monitoring application based on source location [9,10,29].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation on Influence Factors of Acoustic Emission Activity in Coal Failure Process

Yang

Wen

et al. 2018

Energies

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Stress-dominated coal and gas outburst disaster has become one of the main safety problems in deep coal mines. Acoustic emission (AE) or microseismic technology has been viewed as a promising method that can effectively reflect the stress and stability status of rock mass. The AE activity precursor of coal failure is the theoretical basis of this technology. In this study, AE experiments in failure process of coal specimens with different properties and under different stress conditions were performed in laboratory to explore influence factors and their effect of AE activity, and AE activity pattern classification was proposed based on the failure type of coal. The results indicate that the AE activity of different coals under loading are associated with the failure phase, and the evolution pattern of AE activity depends on the failure type of stressed coal. Both the mechanical property and the external stress condition have an important influential effect on the failure type and AE activity pattern in coal failure process. The internal mechanical property decides the inherent tendency of stressed coals to perform brittle or ductile behavior, and the responded AE activity pattern. The contrast of fissure distribution of specimens suggested that fissure structure in coal significantly affects the fracturing mode of coal in uniaxial compression and the AE activity pattern. The external stress condition has a transition effect on AE event energy distribution and AE activity pattern. Under the effect of external stress condition, the energy distribution of AE events was transforming between relative disperse and relative concentration, the failure type and AE activity evolution pattern of coal could appear the brittle-ductile transition. Based on the view of failure type, the pattern of AE activity of coal failure can be classified into three types, i.e., ductile, brittle, and semi-brittle pattern. It is suggested that the high-level AE activity can be viewed as the precursor of brittle instability of coal, and relative quiet phenomenon of AE activity as the precursor of ductile or semi-brittle instability. The research achievement can provide a theoretical base for the prewarning criteria establishment of coal and rock dynamic disasters at depth and improve the insight of AE activity in the coal failure process.

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An Engineering Approach to Seismic Risk Management in Hardrock Mines

Cited by 113 publications

References 5 publications

Microseismic monitoring and stability evaluation for the large scale underground caverns at the Houziyan hydropower station in Southwest China

Microseismic monitoring and stability evaluation for the large scale underground caverns at the Houziyan hydropower station in Southwest China

K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)

Experimental Investigation on Influence Factors of Acoustic Emission Activity in Coal Failure Process

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