A location-dependent earthquake prediction using recurrent neural network algorithms
In this paper, we propose a location-dependent earthquake prediction based on recurrent
neural network algorithms. The location-dependent prediction consists of clustering the …
neural network algorithms. The location-dependent prediction consists of clustering the …
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms
MA Murti, R Junior, AN Ahmed, A Elshafie - Scientific reports, 2022 - nature.com
Earthquake is one of the natural disasters that have a big impact on society. Currently, there
are many studies on earthquake detection. However, the vibrations that were detected by …
are many studies on earthquake detection. However, the vibrations that were detected by …
Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment
C Ning, Y Xie - Computer‐Aided Civil and Infrastructure …, 2024 - Wiley Online Library
This study develops an end‐to‐end deep learning framework to learn and analyze ground
motions (GMs) through their latent features, and achieve reliable GM classification …
motions (GMs) through their latent features, and achieve reliable GM classification …
Automatic Classification of Microseismic Signals Based on MFCC and GMM‐HMM in Underground Mines
In order to mitigate economic and safety risks during mine life, a microseismic monitoring
system is installed in a number of underground mines. The basic step for successfully …
system is installed in a number of underground mines. The basic step for successfully …
Review of machine learning and deep learning application in mine microseismic event classification
W Jinqiang, P Basnet, S Mahtab - Mining of Mineral Deposits, 2021 - ir.nmu.org.ua
Purpose. To put forward the concept of machine learning and deep learning approach in
Mining Engineering in order to get high accuracy in separating mine microseismic (MS) …
Mining Engineering in order to get high accuracy in separating mine microseismic (MS) …
Establishing site response-based micro-zonation by applying machine learning techniques on ambient noise data: a case study from Northern Potwar Region …
Several major earthquakes have jolted Pakistan during the last 30 years, destroyed
infrastructure and severe damage to the economy. Despite advancement in data sciences …
infrastructure and severe damage to the economy. Despite advancement in data sciences …
Identification and spatio-temporal analysis of earthquake clusters using SOM–DBSCAN model
Seismic catalogs are vital to understanding and analyzing the progress of active fault
systems. The background seismicity rate in a seismic catalog, strongly associated with …
systems. The background seismicity rate in a seismic catalog, strongly associated with …
Cluster analysis of earthquake ground-motion records and characteristic period of seismic response spectrum
Classification of earthquake ground-motion records is carried out in this article using K-
means cluster technique. Two kinds of cluster attributes are introduced, which are …
means cluster technique. Two kinds of cluster attributes are introduced, which are …
Machine learning approach for sequence clustering with applications to ground-motion selection
Clustering analysis of sequential data is of great interest and importance in many science
and engineering areas thanks to the explosive growth of time-series data. Effective methods …
and engineering areas thanks to the explosive growth of time-series data. Effective methods …
Identification of pulse-like ground motions using artificial neural network
For more than 20 years, the concept of near-fault pulse-like ground motion has been a topic
of great interest due to its distinct characteristics, particularly due to directivity or fling effects …
of great interest due to its distinct characteristics, particularly due to directivity or fling effects …