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首页> 外文会议>EUR 21431; Final Conference of Cost Action C12; 20050120-22; Innsbruck(AT) >Real-time model for earthquake prediction
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Real-time model for earthquake prediction

机译：地震预报实时模型

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摘要

The paper is devoted on the problem of real-time earthquake prediction. Different kinds of stages of earthquake prediction are observed, and implementation of existing algorithms M8 and MSc for intermediate-term middle-range prediction for is discussed. An approach for real-time prognoses, based on neural network and vector quantization is suggesting. As input information for the neural network are given the parameters of recorded part of accelerogram, principle axis transformation and spectral characteristics of the wave. With the help of stochastic long-range dependence time series analyses and scene oriented model are determined the boundaries of destructive phase of strong motion acceleration. For selected diapason of transformed accelerograms is implemented one-dimensional and two-dimensional vector quantization. With self-organized map are determined weight centers of selected classes. The prognoses are realized with the help of neural network, learned and trained to optimize selected target classes and determine probability density function.
机译：本文致力于实时地震预报问题。观察了地震预测的不同阶段，并讨论了用于中期中期预测的现有算法M8和MSc的实现。提出了一种基于神经网络和矢量量化的实时预测方法。作为神经网络的输入信息，给出了加速度计记录部分的参数，主轴变换和波的频谱特性。借助随机的长期相关性时间序列分析和面向场景的模型，确定了强运动加速的破坏阶段的边界。对于变换后的加速度计的选定diapason，执行一维和二维矢量量化。通过自组织图，可以确定选定类别的重心。预后借助于神经网络来实现，经过学习和培训可以优化所选目标类别并确定概率密度函数。

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