Clustering analysis of the seismic catalog of Iran

Ansari, Anooshiravan; Noorzad, Ali; Zafarani, Hamid

doi:10.1016/j.cageo.2008.01.010

Cited by 55 publications

(22 citation statements)

References 28 publications

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“…It is known that non-uniform status of seismic monitoring in an area introduce factors of inconsistency and incompleteness in any earthquake catalog, resulting into inherent uncertainty in the search for 'long-term earthquake clustering' (Kagan and Jackson, 1991). Further, any statistical treatment for cluster analysis essentially depends on the completeness of the earthquake catalogue (Ansari et al 2009). For this purpose, suitable magnitude for catalogue completeness has been carried out as per the GutenbergRichter relationship.…”

Section: Seismic Cluster Analysismentioning

confidence: 98%

Seismic Clusters and their Characteristics at the Arabian Sea Triple Junction: Supportive Evidences for Plate Margin Deformations

Mukhopadhyay

Dasgupta

2011

Journal of the Geological Society of India

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The plate margin features defining the Arabian Sea Triple Junction (ASTJ) are: the Aden Ridge (AR), Sheba Ridge (SR) with their intervening Alula-Fartak Transform (AFT), Carlsberg Ridge (CR) and Owen Fracture Zone (OFZ). Exact nature of ASTJ is presently debated: whether it is RRF (ridge-ridge-fault) or RRR (ridge-ridge-ridge) type. A revised seismicity map for ASTJ is given here using data for a period little more than a century. "Point density spatial statistical criterion" is applied to short-listed 742 earthquakes (mb ≥ 4.3), 10 numbers of spatio-temporal seismic clusters are identified for ASTJ and its arms. Relocated hypocentres help better constraining the cluster identification wherever such data exist. Seismic clusters actually diagnose the most intense zones of strain accumulation due to far field as well as the local stress operating at ASTJ. An earthquake swarm emanating from a prominent seismic cluster below SR provides an opportunity to investigate the pore pressure diffusion process (due to the active source) by means of "r-t plot". Stress and faulting pattern in the active zones are deduced from 43 CMT solutions. While normal or lateral faulting is characteristic for these arms, an anomalous thrust earthquake occurs in the triangular 'Wheatley Deep' deformation zone proximal to ASTJ. The latter appears to have formed due to a shift of the deformational front from OFZ towards a transform that offsets SR. Though ASTJ is still in the process of evolution, available data favour that this RRF triple junction may eventually be converted to a more stable RRR type.

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Section: Seismic Cluster Analysismentioning

confidence: 98%

Seismic Clusters and their Characteristics at the Arabian Sea Triple Junction: Supportive Evidences for Plate Margin Deformations

Mukhopadhyay

Dasgupta

2011

Journal of the Geological Society of India

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“…SOM is another widely used seismic cluster method, and Zamani and Hashemi [5] introduced a self-organized tectonic zoning for Iran, and then Zamani et al [18] chose Wilk's Lambda criterion and a relative discrepancy of Wilk's Lambda to determine the optimum tectonic zoning number; and Mojarab et al [19] discussed the effect of SOM input parameters. Fuzzy clusters have also been used in seismic clusters, Ansari et al [4] and Benitez et al [21] used the Gath and Giva (GG) fuzzy cluster for Iran and South West Colombia, respectively; while Monem and Hashemy [20] applied the fuzzy -means (FCM) and the GustafsonKessel (GK) clusters to the Ghazvin canal irrigation network. In addition, the density-based algorithms [22][23][24] and the TriGen-based method [25] have also been applied for the seismic cluster.…”

Section: State Of the Artmentioning

confidence: 99%

“…Microseismic monitoring has been widely used in mining [1][2][3], where seismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment [4]. Although this task can be implemented by an expert visually, the expert knowledge-based cluster is nonquantitative, subjective, and hard to interpret for a large amount of data [5].…”

Section: Introductionmentioning

confidence: 99%

“…Although this task can be implemented by an expert visually, the expert knowledge-based cluster is nonquantitative, subjective, and hard to interpret for a large amount of data [5]. Intelligent cluster analysis, taking advantages of spatial, temporal, and other features of seismic events, is more highlighted due to its advances in extracting useful knowledge and finding hidden information from numerous seismic data [4]. Motivated by these facts, various cluster methods have been introduced/proposed for seismicity partitioning, among which -Means cluster is the most popular one.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…This subsection is concluded with a mention to the following studies dealing with the unsupervised classification problem: (Ansari et al, 2009;Esposito et al, 2007;2005;Orozco-Alzate & Castellanos-Domínguez, 2007). They are aimed at finding clusters in seismic volcanic data and understanding their structure.…”

Section: Publicationmentioning

confidence: 99%

The Automated Identification of Volcanic Earthquakes: Concepts, Applications and Challenges

Orozco-Alzate¹,

Acosta-Muñoz²,

Londoño³

2012

Earthquake Research and Analysis - Seismology, Seismotectonic and Earthquake Geology

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Clustering analysis of the seismic catalog of Iran

Cited by 55 publications

References 28 publications

Seismic Clusters and their Characteristics at the Arabian Sea Triple Junction: Supportive Evidences for Plate Margin Deformations

Seismic Clusters and their Characteristics at the Arabian Sea Triple Junction: Supportive Evidences for Plate Margin Deformations

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

The Automated Identification of Volcanic Earthquakes: Concepts, Applications and Challenges

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