Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China
Jian Sheng1, Dongmei Mu*, 2, Hongyan Zhang1, Han Lv1
Identifiers and Pagination:Year: 2015
First Page: 522
Last Page: 528
Publisher Id: TOCIEJ-9-522
Article History:Received Date: 16/9/2014
Revision Received Date: 23/12/2014
Acceptance Date: 31/12/2014
Electronic publication date: 31/7/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub regiondividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earthquake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and prediction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for different prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller subregions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.