Vijay Rahul Kumar, Nanda Satyasai Jagannath
Department of Computer Science, Banasthali Vidyapith, Tonk, Rajasthan 304022 India.
Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Rajasthan, 302017 India.
Pattern Anal Appl. 2023;26(1):19-37. doi: 10.1007/s10044-022-01092-1. Epub 2022 Jul 17.
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 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.
本文提出了一种子序列时间序列聚类算法，用于从活跃地区的地震目录中识别强耦合余震序列和泊松背景活动。该方法考虑连续事件对之间的事件间隔时间统计，以表征时间序列的性质，并同时观察它们与地震震中和震级信息的相关性。这种方法将长地震时间序列分类为有限的有意义的时间序列，然后将聚类机制应用于选定的序列。该方法基于两个阶段构建：（1）基于高斯核的密度估计，用于找到给定地震时间序列的最优子序列；（2）对每个子序列进行事件间隔时间（ ）和基于距离的观测，以检查其中高度相关的余震序列（热点）的存在。基于变异系数（COV）确定余震的存在。在选定的子序列上应用一个以地震震级 为窗口的滑动时间窗口，以滤除时间相关事件的存在，并生成有意义的时间平稳泊松子序列。该方法应用于苏门答腊-安达曼地区（2000 - 2021年）和全球ISC - GEM（2000 - 2016年）地震目录。模拟结果表明，在 的指数分布中实现线性累积率和与时间无关的 后，有意义的子序列（背景事件）可以用齐次泊松过程建模。对于两个研究目录都实现了 和 的关系。对比分析证明了该方法相对于现有方法和最近引入的方法具有竞争力的性能。
