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首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Recurrent Neural Network For Approximate Earthquake Time And Location Prediction Usingmultiple Seismicity Indicators
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Recurrent Neural Network For Approximate Earthquake Time And Location Prediction Usingmultiple Seismicity Indicators

机译：利用多个地震指标的递归神经网络进行近似地震时间和位置预测

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摘要

A computational approach is presented for predicting the location and time of occurrence of future moderate-to-large earthquakes in an approximate sense based on neural network modeling and using a vector of eight seismicity indicators as input. Two different methods are explored. In the first method, a large seismic region is subdivided into several small subregions and the temporal historical earthquake record is divided into a number of small equal time periods. Seismicity indicators are computed for each subregion for each time period and their relationship to the magnitude of the largest earthquake occurring in that subregion during the following time-period is studied using a recurrent neural network. In the second more direct approach, the temporal historical earthquake record is divided into a number of unequal time periods where each period is defined as the time between large earthquakes. Seismicity indicators are computed for each time-period and their relationship to the latitude and longitude of the epicentral location, and time of occurrence of the following major earthquake is studied using a recurrent neural network.
机译：提出了一种计算方法，用于基于神经网络建模并使用八个地震指标的矢量作为输入，在近似意义上预测未来中到大型地震的发生的位置和时间。探索了两种不同的方法。在第一种方法中，将大地震区域细分为几个小子区域，并将时间历史地震记录划分为多个小的相等时间段。在每个时间段内为每个子区域计算地震指标，并使用递归神经网络研究它们与随后一段时间在该子区域内发生的最大地震的大小的关系。在第二种更直接的方法中，时间历史地震记录被分为多个不相等的时间段，其中每个时间段被定义为大地震之间的时间。计算每个时间周期的地震指标，以及它们与震中位置的纬度和经度的关系，并使用递归神经网络研究以下大地震的发生时间。

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