【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。更多还原

【Abstract】 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.更多还原