基于神经网络的近震与远震识别
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摘要
选用P波震相附近的地震波作为研究对象,对近震和远震特征信息进行探讨。选取初至P波主周期作为神经网络输入元,P波到达后2～6s作为地震波时间窗,选择正确的网络结构和参数,搜集大量的地震样本数据进行训练,实现对近震和远震地震事件的非线性系统识别。结果表明:在样本训练区间检验数据的预测结果置信度达到100%;在非样本区间也能迅速收敛到标识符0或1附近。近震样本信号最大周期为0.25s,而置信度达到80%以上的预测区间几乎接近0.35s;远震样本信号最小周期为0.9s,而置信度达到80%以上的预测区间达到0.5s,表明模型建立得当,具有良好的泛化能力。
We studied seismic waves near the P wave phase to discuss the characteristic of near and distant earthquakes.Firstly,we selected main period of initial P wave as the input element of neural network and picked up 2 ~ 6 s of initial P wave arriving time as time window of seismic wave.Secondly,we chose the right network structure and parameters and collected a large number of earthquake training data to realize the nonlinear system identification between near and distant earthquake.The results show that the predictable result of test data whose confidence reaches 100% in sample training interval can converge to identifier 0 or 1 quickly in non-sample training interval.The maximum period of near earthquake sample is 0.25 s and its prediction interval whose confidence reaches more than 80% is almost close to 0.35 s.The minimum period of distant earthquake sample is 0.9 s and its prediction interval whose confidence reaches more than 80% is 0.5 s,which shows that the model we select is proper and has good generalization ability.
We studied seismic waves near the P wave phase to discuss the characteristic of near and distant earthquakes.Firstly,we selected main period of initial P wave as the input element of neural network and picked up 2 ~ 6 s of initial P wave arriving time as time window of seismic wave.Secondly,we chose the right network structure and parameters and collected a large number of earthquake training data to realize the nonlinear system identification between near and distant earthquake.The results show that the predictable result of test data whose confidence reaches 100% in sample training interval can converge to identifier 0 or 1 quickly in non-sample training interval.The maximum period of near earthquake sample is 0.25 s and its prediction interval whose confidence reaches more than 80% is almost close to 0.35 s.The minimum period of distant earthquake sample is 0.9 s and its prediction interval whose confidence reaches more than 80% is 0.5 s,which shows that the model we select is proper and has good generalization ability.
引文
蔡煜东,宫家文,甘俊人,等.1994.地震序列类型预测的人工神经网络方法[J].地震研究,17(1):40-45.
何斌,张元生,李稳.2010.计算地震初至波视出射角方法[J].西北地震学报,32(1):11-15.
刘希强,周惠兰,沈萍,等.2000.用于三分向记录震相识别的小波变换方法[J].地震学报,32(2):125-131.
穆星.2005.利用地震几何属性和自组织神经网络进行地震相得自动识别[J].地质科技情报,24(3):109-112.
王娟,刘俊民,范万春.2004.神经网络在震相识别中的应用[J].网络与通信,(5):35-37.
赵仲和.2005.地震自动定位的综合解决方案[J].地震地磁观测与研究,26(2):50-56.
中国地震局监测预报司.2007.地震学与地震观测[M].北京:地震出版社.
Allen.1978.Automatic earthquake recognition and timing from singletraces[J].BSSA,68(5):1521-1532.
Cichowicz.1993.An automatic S-phase picker[J].BSSA,83(1):180-189.
Coforth T,Herin E.1981.An automatic seismic signal detection algorithmbased on the walsh transform[J].BSSA,71(4):1351-1360.
Jin W,Ta-Liang T.1988.Artificial neutral network based seismic detector[J].BSSA,85(1):308-319.
何斌,张元生,李稳.2010.计算地震初至波视出射角方法[J].西北地震学报,32(1):11-15.
刘希强,周惠兰,沈萍,等.2000.用于三分向记录震相识别的小波变换方法[J].地震学报,32(2):125-131.
穆星.2005.利用地震几何属性和自组织神经网络进行地震相得自动识别[J].地质科技情报,24(3):109-112.
王娟,刘俊民,范万春.2004.神经网络在震相识别中的应用[J].网络与通信,(5):35-37.
赵仲和.2005.地震自动定位的综合解决方案[J].地震地磁观测与研究,26(2):50-56.
中国地震局监测预报司.2007.地震学与地震观测[M].北京:地震出版社.
Allen.1978.Automatic earthquake recognition and timing from singletraces[J].BSSA,68(5):1521-1532.
Cichowicz.1993.An automatic S-phase picker[J].BSSA,83(1):180-189.
Coforth T,Herin E.1981.An automatic seismic signal detection algorithmbased on the walsh transform[J].BSSA,71(4):1351-1360.
Jin W,Ta-Liang T.1988.Artificial neutral network based seismic detector[J].BSSA,85(1):308-319.
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