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首页> 外文期刊>International Journal of Neural Systems >NEURAL NETWORK MODELS FOR EARTHQUAKE MAGNITUDE PREDICTION USING MULTIPLE SEISMICITY INDICATORS
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NEURAL NETWORK MODELS FOR EARTHQUAKE MAGNITUDE PREDICTION USING MULTIPLE SEISMICITY INDICATORS

机译：基于多个地震指标的地震震级预报神经网络模型

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摘要

Neural networks are investigated for predicting the magnitude of the largest seismic event in the following month based on the analysis of eight mathematically computed parameters known as seismicity indicators. The indicators are selected based on the Gutenberg-Richter and characteristic earthquake magnitude distribution and also on the conclusions drawn by recent earthquake prediction studies. Since there is no known established mathematical or even empirical relationship between these indicators and the location and magnitude of a succeeding earthquake in a particular time window, the problem is modeled using three different neural networks: a feed-forward Levenberg-Marquardt backpropagation (LMBP) neural network, a recurrent neural network, and a radial basis function (RBF) neural network. Prediction accuracies of the models are evaluated using four different statistical measures: the probability of detection, the false alarm ratio, the frequency bias, and the true skill score or R score. The models are trained and tested using data for two seismically different regions: Southern California and the San Francisco bay region. Overall the recurrent neural network model yields the best prediction accuracies compared with LMBP and RBF networks. While at the present earthquake prediction cannot be made with a high degree of certainty this research provides a scientific approach for evaluating the short-term seismic hazard potential of a region.
机译：基于对八个数学计算参数（称为地震活动性指标）的分析，对神经网络进行了研究，以预测下个月最大的地震事件的强度。根据古腾堡-里希特（Gutenberg-Richter）和特征地震震级分布以及最近的地震预测研究得出的结论选择指标。由于这些指标与特定时间窗口内后续地震的位置和震级之间没有已知的已建立的数学甚至经验关系，因此可以使用三种不同的神经网络对问题进行建模：前馈Levenberg-Marquardt反向传播（LMBP）神经网络，递归神经网络和径向基函数（RBF）神经网络。使用四种不同的统计量度来评估模型的预测准确性：检测概率，错误警报率，频率偏差以及真实技能得分或R得分。使用两个地震不同地区的数据对模型进行了训练和测试：南加州和旧金山湾地区。总体而言，与LMBP和RBF网络相比，递归神经网络模型可产生最佳的预测精度。尽管目前尚不能高度确定地震的预测，但这项研究为评估一个地区的短期地震危险性提供了一种科学的方法。

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