5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm

The detection of earthquake precursor signals a few days before the earthquake day is one of the most studied subjects today. In recent years, a strong correlation is observed between earthquakes and ionospheric parameters. In this study, a feed-forward back propagation artificial neural network (AN...

Full description

Saved in:
Bibliographic Details
Published inIEEE geoscience and remote sensing letters Vol. 20; p. 1
Main Authors Karatay, Secil, Saide Eda Gul
Format Journal Article
LanguageEnglish
Published Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.01.2023
Subjects
Algorithms
Anomalies
Artificial neural networks
Back propagation networks
Bayesian analysis
Bayesian theory
Earthquake prediction
Earthquakes
Global positioning systems
GPS
Ionospheric propagation
Neural networks
Positioning systems
Probability theory
Regularization
Seismic activity
Total Electron Content
Online AccessGet full text
ISSN1545-598X
1558-0571
DOI10.1109/LGRS.2023.3262028

Cover

More Information
Summary:The detection of earthquake precursor signals a few days before the earthquake day is one of the most studied subjects today. In recent years, a strong correlation is observed between earthquakes and ionospheric parameters. In this study, a feed-forward back propagation artificial neural network (ANN) Bayesian regularization (BR) algorithm is applied to detect the seismic disturbances and anomalies by predicting global positioning system (GPS)-total electron content (TEC) on earthquake days with magnitude greater than 5. It is observed that TEC is predicted with greater error margins for the stations at a maximum distance of 50 km from the epicenters. The errors for earthquakes less than Mw 7 are smaller than those for greater than 7.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3262028