前驱波的小波能量与支持向量机分类  

Classification of the earthquake precursor wave based on wavelet transform and support vector machine

作　　者：张宇翔[1] 晋琅[2] 黄力宇[2] 郑佳宁[2] 

机构地区：[1]陕西省地震局地震监测中心,陕西西安710068 [2]西安电子科技大学,陕西西安710071

出　　处：《西北大学学报（自然科学版）》2012年第6期935-939,共5页Journal of Northwest University（Natural Science Edition）

基　　金：国家自然科学基金资助项目(81071221)

摘　　要：目的通过对震前地壳内长周期形变前驱波检测新方法与波形分析的研究,为未来地震预测寻找一个突破点。方法分析了采集到的超低频加速度前驱波信号,发现直接利用前驱波异常信号与地震发生的关系研究得到其关联度仅为51.59%;为提高分析效果,通过小波变换方法对前驱波异常信号进行分解,提取其各分量能量信息,并以此作为特征向量输入支持向量机进行信号分类。结果研究表明利用创新的前驱波检测技术和基于小波分解及支持向量机实现信号分类,异常前驱波与地震发生之间的关联度可达72.028%。结论所提出的方法在探索地震发生的前兆信息方面有一定价值,但在扩大采集地、更多的数据输入、核函数及其相关参数的优化选择等问题尚待进一步的研究。Aim The earthquake prediction still remains a science problem worldwide, a new detection method and waveform analysis of long period crust deformation precursor wave before the event may become a breakthrough in future earthquake prediction. Methods In the analysis of the signal collected based on this technology, it is found that the accuracy of earthquake prediction using the relationship between the precursor abnormal signals and the earthquake is 51.59% ; but in the classification based on the wavelet transform and support vector machine （SVM） method, the precursor wave abnormal signal is first decomposed using wavelet transform, and then the energy of each level is calculated and become the input of the SVM to be classified. Results The result shows that the accu- racy of earthquake prediction can come to 72. 028% by using the innovative precursor wave detection technology and the combined signal processing method of wavelet transform and SVM. Conclusion The proposed method has a certain advantage in predicting earthquake. Additional studies need to be carried out to further evaluate and im- prove the approach.

关 键 词：地震预测 前驱波 小波变换 支持向量机 

分 类 号：P315[天文地球—地震学]