基于BP-Adaboost方法的天然地震和人工爆炸事件波形信号分类识别研究

BP神经网络和支持向量机(SVM)是两种主流的分类识别方法,用于天然地震和人工爆炸事件波形信号分类识别时取得了较好的效果。但BP神经网络存在易陷入局部最优及隐层数和隐层节点数与训练样本数据密切相关而无法有效预先确定;而支持向量机(SVM)方法则缺乏有效手段来选取合适的核函数,从中不能很好地扩展到多分类。针对天然地震和人工爆炸事件波形信号的分类识别问题,文中将上述两种方法和集成学习——BP-Adaboost方法进行了对比实验研究。据对所选用的地震、爆炸事件波形信号数据集的分类识别结果表明,BP-Adaboost方法得到了98%以上的正确识别率,并且具有较好的泛化能力。相较于BP神经网络和PCA-SVM方法,BP-Adaboost方法对于数据集的划分和识别结果具有更好的鲁棒性,应用于天然地震和人工爆炸事件波形信号分类识别时,可取得更好的识别效果。同时,结合Adaboost方法的原理,阐述了BP-Adaboost方法拥有更好分类结果和泛化能力的原因。

Research on the Classification of Seismic Wave Signals of Earthquakes and Explosion Events Based on BP-Adaboost

Back-propagation neural-networks (BP-NN) and the support vector machine (SVM) are the two mainstream methods for classification of seismic wave signals of earthquakes and explosion events used in this research. The two methods achieved accurate and effective results. However, when training the BP-NN, it is inevitable that it can be easily trapped in a local optimum; in addition, the optimal numbers of hidden layers and numbers of nodes in each layer are heavily dependent on the distribution configuration of the training samples data, and cannot be consistently determined in advance. Furthermore, when training the SVM, there is a shortage of effective means to select suitable kernel function(s); hence, the ordinary SVM cannot be easily extended to multiclass problems. Aiming at the classification of seismic wave signals of earthquakes and explosion events, this paper investigates and compares the BP-NN and the SVM, along with the BP-Adaboost ensemble learning method. Using the dataset of seismic wave signals of earthquakes and explosion events in the experiments of this paper, the classification results show that the BP-Adaboost method can achieve the overall correct recognition rate of not less than 98%, with excellent generalization ability. Compared with BP-NN and SVM, the two main traditional classification methods, it has been shown that the BP-Adaboost method is more robust for different dataset partitions and corresponding classification, which implies more robust generalizability and better classification of seismic wave signals of earthquakes and explosion events. In the meanwhile, the theory of the Adaboost method is applied to explain the reasons for the better classification results and the generalizability of the BP-Adaboost method.