A spatially focused clustering methodology for mining seismicity

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Mining seismicity is routinely observed to cluster in space and time due to the spatially distinct rock mass failure processes associated with the temporally dependent process of mining. Assessment of clustered seismicity is important to develop an understanding of and to quantify seismic hazard that is associated with mining. This article presents a density-based clustering method that is applicable to the assessment of 3D spatial distributions of short-term seismicity. The methodology presented in this article is developed from existing approaches that address the general limitations of density-based clustering algorithms. Synthetically generated seismicity allows for the assessment of the methodology with respect to external and internal performance measures. The clustering of a dataset with known attributes allows for confidence to be developed in the capability of the clustering method. Additionally, this internal performance evaluation can represent the relative accuracy of outcomes without prior information concerning dataset attributes. The clustering method is applied to two case studies of mining seismicity. These cases illustrate the general applicability of the clustering method along with the value of evaluating internal performance measures when optimising the selection of parameters and understanding the sensitivity of clustering outcomes to these choices.

Original languageEnglish
Pages (from-to)104-113
Number of pages10
JournalEngineering Geology
Volume232
DOIs
Publication statusPublished - 8 Jan 2018

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seismicity
methodology
Clustering algorithms
Spatial distribution
Hazards
seismic hazard
Rocks
spatial distribution
method
rock
attribute

Cite this

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title = "A spatially focused clustering methodology for mining seismicity",
abstract = "Mining seismicity is routinely observed to cluster in space and time due to the spatially distinct rock mass failure processes associated with the temporally dependent process of mining. Assessment of clustered seismicity is important to develop an understanding of and to quantify seismic hazard that is associated with mining. This article presents a density-based clustering method that is applicable to the assessment of 3D spatial distributions of short-term seismicity. The methodology presented in this article is developed from existing approaches that address the general limitations of density-based clustering algorithms. Synthetically generated seismicity allows for the assessment of the methodology with respect to external and internal performance measures. The clustering of a dataset with known attributes allows for confidence to be developed in the capability of the clustering method. Additionally, this internal performance evaluation can represent the relative accuracy of outcomes without prior information concerning dataset attributes. The clustering method is applied to two case studies of mining seismicity. These cases illustrate the general applicability of the clustering method along with the value of evaluating internal performance measures when optimising the selection of parameters and understanding the sensitivity of clustering outcomes to these choices.",
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A spatially focused clustering methodology for mining seismicity. / Woodward, Kyle; Wesseloo, Johan; Potvin, Yves.

In: Engineering Geology, Vol. 232, 08.01.2018, p. 104-113.

Research output: Contribution to journalArticle

TY - JOUR

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AU - Woodward, Kyle

AU - Wesseloo, Johan

AU - Potvin, Yves

PY - 2018/1/8

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AB - Mining seismicity is routinely observed to cluster in space and time due to the spatially distinct rock mass failure processes associated with the temporally dependent process of mining. Assessment of clustered seismicity is important to develop an understanding of and to quantify seismic hazard that is associated with mining. This article presents a density-based clustering method that is applicable to the assessment of 3D spatial distributions of short-term seismicity. The methodology presented in this article is developed from existing approaches that address the general limitations of density-based clustering algorithms. Synthetically generated seismicity allows for the assessment of the methodology with respect to external and internal performance measures. The clustering of a dataset with known attributes allows for confidence to be developed in the capability of the clustering method. Additionally, this internal performance evaluation can represent the relative accuracy of outcomes without prior information concerning dataset attributes. The clustering method is applied to two case studies of mining seismicity. These cases illustrate the general applicability of the clustering method along with the value of evaluating internal performance measures when optimising the selection of parameters and understanding the sensitivity of clustering outcomes to these choices.

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