Tetra‐stage cluster identification model to analyse the seismic activities of Japan, Himalaya and Taiwan

Vijay, Rahul Kumar; Nanda, Satyasai Jagannath

doi:10.1049/iet-spr.2016.0639

Cited by 16 publications

(2 citation statements)

References 27 publications

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“…Here, the performance of the proposed method is also compared with the recently introduced algorithms by Vijay and Nanda [ 30 ] that are based on the K-means clustering approach. This method has very less execution time for declustering but the results are highly dependent on the selection of the cluster centroids at the beginning.…”

Section: Comparison Of the Proposed Methods With State-of-art Approachesmentioning

confidence: 99%

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Earthquake pattern analysis using subsequence time series clustering

Vijay

Nanda

2022

Pattern Anal Applic

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In this paper, a subsequence time-series clustering algorithm is proposed to identify the strongly coupled aftershocks sequences and Poissonian background activity from earthquake catalogs of active regions. The proposed method considers the inter-event time statistics between the successive pair of events for characterizing the nature of temporal sequences and observing their relevance with earthquake epicenters and magnitude information simultaneously. This approach categorizes the long-earthquake time series into the finite meaningful temporal sequences and then applies the clustering mechanism to the selective sequences. The proposed approach is built on two phases: (1) a Gaussian kernel-based density estimation for finding the optimal subsequence of given earthquake time-series, and (2) inter-event time ( ) and distance-based observation of each subsequence for checking the presence of highly correlated aftershock sequences (hot-spots) in it. The existence of aftershocks is determined based on the coefficient of variation (COV). A sliding temporal window on with earthquake’s magnitude M is applied on the selective subsequence to filter out the presence of time-correlated events and make the meaningful time stationary Poissonian subsequences. This proposed approach is applied to the regional Sumatra-Andaman (2000–2021) and worldwide ISC-GEM (2000–2016) earthquake catalog. Simulation results indicate that meaningful subsequences (background events) can be modeled by a homogeneous Poisson process after achieving a linear cumulative rate and time-independent in the exponential distribution of . The relations and are achieved for both studied catalogs. Comparative analysis justifies the competitive performance of the proposed approach to the state-of-art approaches and recently introduced methods.

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Section: Comparison Of the Proposed Methods With State-of-art Approachesmentioning

confidence: 99%

“…This approach is highly dependent on the size of the earthquake catalog. Vijay and Nanda also reported several statistical and swarm-intelligence-based declustering models for Spatio-temporal seismicity analysis of different seismically active regions [ 30 – 32 ].…”

Section: Introductionmentioning

confidence: 99%