Earthquake pattern analysis using subsequence time series clustering

Abstract

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.

A Novel and Efficient Spatio-Temporal Clustering Algorithm to Discover Patterns in Indian Earthquake Data

“…A study is conducted on earthquake time-series analysis, specifically de-clustering sequences and regular background events that follow a Poisson process in the time domain. This research relies on COV(T) (coefficient of variation of inter-event times) and inter-event time statistics using a sliding temporal window method [16].…”

An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events

“…Time series prediction (TSP) involves analyzing historical time series data to discover patterns and predict future values. TSP has commonly been utilized in various fields such as stock price prediction [1], weather forecasting [2], earthquake prediction [3], river water level forecasting [4], physiological symptoms detection [5], and more. Monitoring and forecasting network events are imperative in network intrusion to understand future attack trends.…”