K-means cluster analysis and seismicity partitioning for Pakistan

Rehman, Khaista; Burton, Paul W.; Weatherill, Graeme

doi:10.1007/s10950-013-9415-y

Cited by 50 publications

(29 citation statements)

References 48 publications

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“…Weatherill and Burton [6] implemented the -Means cluster to provide a catalogue of earthquakes for Aegea, and results demonstrate that from 20 to 30 clusters are the most appropriate number in capturing the hypocentral distribution and fault type. Rehman et al [7] applied the -Means cluster to estimate seismic hazard and risk in Pakistan and found that 19 clusters were the best to include the sufficient knowledge of subsurface geological structure and geophysical information. Morales-Esteban et al [8] proposed an adaptive Mahalanobis -Means algorithm to study the seismic catalogues of the Croatia and Iberian Peninsula, and the result showed the ability of this method to discover not only circular but also elliptical shapes.…”

Section: State Of the Artmentioning

confidence: 99%

“…Therefore, it is hard to determine possible cluster numbers by considering both Silhouette and KL indexes. To solve this problem, a Silhouette and KL combined S-KL index (6) is proposed, which takes advantage of the product of the normalized Silhouette index and KL index, and the normalization is calculated by (7). The relatively large S-KL values correspond to possible optimum cluster numbers.…”

Section: Possible Cluster Number Selectionmentioning

confidence: 99%

“…-Means Cluster Results. Seismicity partitioning with few clusters produces a simple and general classification defining the most basic structure of the area under investigation; however, it is hard to interpret a detailed geological structure, while for a too large cluster number, there may be some clusters that are hard to interpret [7]. Therefore, a balanced cluster number should be selected as the optimum cluster number to interpret the detailed geological structure.…”

Section: S-klmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: State Of the Artmentioning

confidence: 99%

Section: Possible Cluster Number Selectionmentioning

confidence: 99%

Section: S-klmentioning

confidence: 99%

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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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“…Cluster analysis is a useful tool in seismology and has been widely applied to locate earthquakes (Frohlich and Davis 1990;Davis and Frohlich 1991;Dzwinel et al 2005;Shearer et al 2005;Weatherill and Burton 2009;Rehman et al 2014), split shear-waves (Teanby et al 2004) and estimate inversion error (Zheng et al 2015a). Godano et al (2013) demonstrated the presence of multiple earthquake clusters in research on an Italian swarm, while Lindenfeld et al (2012) recorded several similar clusters in a restricted area while researching fluidtriggered earthquake swarms in the East African Rift.…”

Section: Cluster Analysismentioning

confidence: 99%

Rushan earthquake swarm in eastern China and its indications of fluid-triggered rupture

Zheng

et al. 2017

Earthq Sci

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An extraordinary earthquake swarm occurred at Rushan on the Jiaodong Peninsula from October 1, 2013, onwards, and more than 12,000 aftershocks had been detected by December 31, 2015. All the activities of the whole swarm were recorded at the nearest station, RSH, which is located about 12 km from the epicenter. We examine the statistical characteristics of the Rushan swarm in this paper using RSH station data to assess the arrival time difference, t S À P , of Pg and Sg phases. A temporary network comprising 18 seismometers was set up on May 6, 2014, within the area of the epicenter; based on the data from this network and use of the double difference method, we determine precise hypocenter locations. As the distribution of relocated sources reveals migration of seismic activity, we applied the mean-shift cluster method to perform clustering analysis on relocated catalogs. The results of this study show that there were at least 16 clusters of seismic activities between May 6, 2014, and June 30, 2014, and that each was characterized by a hypocenter spreading process. We estimated the hydraulic diffusivity, D, of each cluster using envelope curve fitting; the results show that D values range between 1.2 and 3.5 m 2 /d and that approximate values for clusters on the edge of the source area are lower than those within the central area. We utilize an epidemic-type aftershock sequence (ETAS) model to separate external triggered events from self-excited aftershocks within the Rushan swarm. The estimated parameters for this model suggest that a = 1.156, equivalent to sequences induced by fluid-injection, and that the forcing rate (l) implies just 0.15 events per day. These estimates indicate that around 3% of the events within the swarm were externally triggered. The fact that variation in l is synchronous with swarm activity implies that pulses in fluid pressure likely drove this series of earthquakes.

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“…The K-means cluster analysis has already been successfully applied to the Aegean region (Weatherill and Burton, 2009), Pakistan (Rehman et al, 2013(Rehman et al, , 2017, and the Sumatran fault (Burton and Hall, 2014) where the optimum cluster model correlates well with known structural segmentation of the fault. Here, we apply it to the entire East African rift (EAR) system where there are variations in the extent and quality of structural observations throughout the region.…”

Section: Introductionmentioning

confidence: 99%

Earthquake Clustering and Energy Release of the African–Arabian Rift System

Hall¹,

Nixon

Keir

et al. 2018

Bulletin of the Seismological Society of America

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The earthquakes associated with continental deformation are spatially and temporally variable and are fundamental in understanding fault activity and seismogenic hazards. We conduct K-means cluster analysis on seismicity in the AfricanArabian rift systems to create the first computationally objective analysis of the pattern of earthquakes. We use seismic moment to compute spatial variations in maximum credible earthquake (Mcred) and likely time to the next major release of seismic energy. Our best-fit model has 32 clusters of ∼100-400 km in length, with cluster size decreasing northward along the rift and cluster boundaries correlating with major structural segmentation of the rift. Mcred varies between M w 5.2 and 7.4 across the whole dataset, with the highest values estimated in portions of the rift where the majority of extension is accommodated by seismogenic failure.

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K-means cluster analysis and seismicity partitioning for Pakistan

Cited by 50 publications

References 48 publications

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

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

Rushan earthquake swarm in eastern China and its indications of fluid-triggered rupture

Earthquake Clustering and Energy Release of the African–Arabian Rift System

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