An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series
Joint Authors
Christopoulos, Stavros-Richard G.
Sarlis, Nicholas V.
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-27, 27 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-20
Country of Publication
Egypt
No. of Pages
27
Main Subjects
Abstract EN
Recently, the study of the coherent noise model has led to a simple (binary) prediction algorithm for the forthcoming earthquake magnitude in aftershock sequences.
This algorithm is based on the concept of natural time and exploits the complexity exhibited by the coherent noise model.
Here, using the relocated catalogue from Southern California Seismic Network for 1981 to June 2011, we evaluate the application of this algorithm for the aftershocks of strong earthquakes of magnitude M≥6.
The study is also extended by using the Global Centroid Moment Tensor Project catalogue to the case of the six strongest earthquakes in the Earth during the last almost forty years.
The predictor time series exhibits the ubiquitous 1/f noise behavior.
American Psychological Association (APA)
Christopoulos, Stavros-Richard G.& Sarlis, Nicholas V.. 2017. An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series. Complexity،Vol. 2017, no. 2017, pp.1-27.
https://search.emarefa.net/detail/BIM-1143321
Modern Language Association (MLA)
Christopoulos, Stavros-Richard G.& Sarlis, Nicholas V.. An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series. Complexity No. 2017 (2017), pp.1-27.
https://search.emarefa.net/detail/BIM-1143321
American Medical Association (AMA)
Christopoulos, Stavros-Richard G.& Sarlis, Nicholas V.. An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series. Complexity. 2017. Vol. 2017, no. 2017, pp.1-27.
https://search.emarefa.net/detail/BIM-1143321
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143321