• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Circuits and systems >A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India
【24h】

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

机译：用于印度喜马拉雅山地震震级预测的BP人工神经网络模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.
机译：这项研究的目的是评估BP神经网络技术在预测喜马拉雅带地区发生的地震中的性能（使用不同类型的输入数据）。这些参数是从喜马拉雅地震目录中提取的，该目录由1887年至2015年的过去128年中的喜马拉雅盆地所有小，大事件及其余震序列组成。该数据仓库包含事件数据，事件时间（秒，纬度，经度，深度） ，标准差和大小。这些现场数据被转换为八个数学计算的参数，称为地震指标。这些地震活动性指标已用于训练BP神经网络，以更好地进行决策并预测预定义未来时间段的大小。考虑的这些数学计算指标是基于2.5级以上的每个事件，过去几年至2014年的事件总数，n个事件的频率-幅度分布b值，n个事件的Gutenberg-Richter逆幂定律曲线，平方率的聚类的n次事件释放的地震能量的根，事件释放的能量，n次事件基于古腾堡-里希逆幂定律的回归线的均方差，平均时间的变化系数和震级平均值最后n个事件。我们提出了一个三层前馈BP神经网络模型来识别因素，并以喜马拉雅盆地地区的地震实际震级M和其他七个数学计算的参数地震活动性指标的实际发生为输入和目标向量。通过比较从喜马拉雅地震目录中的地震仪观察到的曲线，该曲线包括2.5级以上的所有事件，2015年喜马拉雅盆地的余震序列以及BP神经网络对2015年地震的预测。该模型对地震产生了良好的预测结果幅度在4.0到6.0之间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号