Abstract
It is a popular problem that the mechanisms of earthquake are still not quite clear. The self-adaptive artificial neural network (ANN) method to combine contributions of various symptom factors of earthquake would be a feasible and useful tool. The back-propagation (BP) neural network can reflect the nonlinear relation between earthquake and various anomalies, therefore physical quantities measured by the DEMETER satellite including Electron density (Ne), Electron temperature (Te), ions temperature (Ti) and oxygen ion density (NO+), are collected to provide sample sets for a BP neural network. In order to improve the speed and the stability of BP neural network, the Levenberg-Marquardt algorithm is introduced to construct the model, and then model validation is performed on near 100 seismic events happened in 2008.
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Ma, L., Xu, F., Wang, X., Tang, L. (2010). Earthquake Prediction Based on Levenberg-Marquardt Algorithm Constrained Back-Propagation Neural Network Using DEMETER Data. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_57
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DOI: https://doi.org/10.1007/978-3-642-15280-1_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15279-5
Online ISBN: 978-3-642-15280-1
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