TY - JOUR
T1 - An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series
AU - Christopoulos, Stavros Richard G.
AU - Sarlis, Nicholas V.
N1 - Publisher Copyright:
© 2017 Stavros-Richard G. Christopoulos and Nicholas V. Sarlis.
PY - 2017/2/20
Y1 - 2017/2/20
N2 - 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 algorithmis 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 algorithmfor the aftershocks of strong earthquakes of magnitudeM ≥ 6. The study is also extended by using the Global CentroidMoment 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.
AB - 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 algorithmis 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 algorithmfor the aftershocks of strong earthquakes of magnitudeM ≥ 6. The study is also extended by using the Global CentroidMoment 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.
KW - Earthquake magnitude
KW - Coherent noise model
KW - Aftershock magnitude time series
UR - http://www.scopus.com/inward/record.url?scp=85019966941&partnerID=8YFLogxK
U2 - 10.1155/2017/6853892
DO - 10.1155/2017/6853892
M3 - Article
AN - SCOPUS:85019966941
VL - 2017
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 6853892
ER -