Abstract
The occurrence of every earthquake has an enormous impact on society and the human community. Thus, there is a need to develop effective methodologies to analyze the spatiotemporal distribution in earthquake-prone regions. In this paper, a three-stage hybrid model based on Fuzzy C-Means (FCM) and Density Peak Clustering (DPC) algorithm is proposed for this analysis. The proposed model considers coordinate, occurrence time, event’s magnitude, and depth for identifying the earthquake aftershock clusters and the background events present in an earthquake catalog. The seismicity analysis of the Philippines and New Zealand earthquake regions is carried out using the proposed model considering the last 30-years data from 1990 to 2020. The obtained results with the proposed model reveal that background events follow a uniform distribution in a short time interval, whereas aftershocks have time-varying distribution in accordance with the occurrence of the main shocks. Comparative analysis has been demonstrated with five other benchmark de-clustering models proposed by Gardner and Knopoff (GK), Uhrahemmer, Raesenberg, Gruenthal, Vijay, and Nanda, respectively.
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Sharma, A., Nanda, S.J., Vijay, R.K. (2021). A Model Based on Fuzzy C-Means with Density Peak Clustering for Seismicity Analysis of Earthquake Prone Regions. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_16
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DOI: https://doi.org/10.1007/978-981-16-2712-5_16
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