Jove
Visualize
Contact Us

Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K
RNA-seq03:21

RNA-seq

10.3K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identification of characteristics frequency and hot-spots in protein sequence of COVID-19 disease.

Biomedical signal processing and control·2022
Same journal

Animal re-identification in video through track clustering.

Pattern analysis and applications : PAA·2025
Same journal

TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approach.

Pattern analysis and applications : PAA·2025
Same journal

JSE: Joint Semantic Encoder for zero-shot gesture learning.

Pattern analysis and applications : PAA·2024
Same journal

Interval regression model adequacy checking and its application to estimate school dropout in Brazilian municipality educational scenario.

Pattern analysis and applications : PAA·2022
Same journal

Grafted and Vanishing Random Subspaces.

Pattern analysis and applications : PAA·2022
Same journal

Probabilistic elderly person's mood analysis based on its activities of daily living using smart facilities.

Pattern analysis and applications : PAA·2021
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Sep 3, 2025

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

9.5K

Earthquake pattern analysis using subsequence time series clustering.

Rahul Kumar Vijay1, Satyasai Jagannath Nanda2

  • 1Department of Computer Science, Banasthali Vidyapith, Tonk, Rajasthan 304022 India.

Pattern Analysis and Applications : PAA
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm to distinguish earthquake aftershocks from background seismic activity using time-series clustering. The method effectively identifies seismic patterns, improving earthquake catalog analysis.

Keywords:
Coefficient of Variation.Earthquake catalogsEarthquake time seriesHomogeneous Poisson processSubsequence clustering

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

Related Experiment Videos

Last Updated: Sep 3, 2025

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

9.5K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

Area of Science:

  • Seismology
  • Data Science
  • Time-Series Analysis

Background:

  • Earthquake catalogs contain complex temporal patterns, including aftershocks and background seismic activity.
  • Distinguishing these event types is crucial for understanding seismicity and seismic hazard.
  • Existing methods may struggle to accurately differentiate between aftershock sequences and background events.

Purpose of the Study:

  • To develop and validate a subsequence time-series clustering algorithm for identifying strongly coupled aftershock sequences and Poissonian background activity.
  • To characterize the temporal nature of earthquake sequences using inter-event time statistics, epicenter, and magnitude information.
  • To categorize long earthquake time series into meaningful temporal subsequences for clustering.

Main Methods:

  • A two-phase approach involving Gaussian kernel-based density estimation for optimal subsequence identification.
  • Inter-event time and distance-based analysis of subsequences to detect highly correlated aftershock sequences (hot-spots).
  • Application of a sliding temporal window with earthquake magnitude to filter time-correlated events and isolate Poissonian subsequences.

Main Results:

  • The proposed algorithm successfully identifies meaningful subsequences (background events) that can be modeled by a homogeneous Poisson process.
  • Achieved linear cumulative rate and time-independent inter-event time distributions for background events.
  • Demonstrated competitive performance compared to state-of-the-art and recently introduced methods on Sumatra-Andaman and ISC-GEM earthquake catalogs.

Conclusions:

  • The developed subsequence clustering algorithm provides an effective means to differentiate earthquake aftershocks from background seismic activity.
  • The method enhances the analysis of earthquake catalogs by accurately modeling background seismicity as a Poisson process.
  • This approach offers a robust tool for seismological research and seismic hazard assessment.