A neural network approach to the identification of electric earthquake precursors.
PHYS CHEM EARTH PT A
315 - 319.
Reliable recognition of Electric Earthquake Precursors (EEP) is mainly prevented by disturbances of magnetotelluric (MT) origin, making EEP's interpretation difficult. However, assuming that EEP's are not accompanied by a significant magnetic anomaly, the MT disturbances can be reduced by applying the method of residual electric field calculations. In the present work, we present a Dynamic Neural Network (DNN) based prediction procedure. A DNN is employed to predict the behaviour of an electrotelluric time series with ultimate objective its application to earthquake prediction using electric precursors. To achieve such a goal, a learning phase is necessary, during which the synaptic weights of the DNN are adjusted according to an appropriate update law so as to minimize an error signal. Then the method proceeds to the prediction phase, where using the weight values resulted at the end of the learning phase the DNN predicts, the actual signal accurately. The proposed approach is applied to measured electrotelluric data. (C) 2000 Elsevier Science Ltd. All rights reserved.
|Title:||A neural network approach to the identification of electric earthquake precursors|
|Keywords:||NONLINEAR DYNAMICAL-SYSTEMS, ADAPTIVE REGULATION, FIELD|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Earth Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Institute for Risk and Disaster Reduction
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