| Automatic signal detection based on support vector machine |
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| 引用本文： | 王海军 刘贵忠. Automatic signal detection based on support vector machine[J]. 地震学报(英文版), 2007, 20(1): 88-97. DOI: 10.1007/s11589-007-0088-x |
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| 作者姓名： | 王海军 刘贵忠 |
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| 作者单位： | Xi'an Jiaotong University Xi'an 710049 China Northwest Institute of Nuclear Technology Xi'an 710024 China，Xi'an Jiaotong University Xi'an 710049 China |
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| 摘 要： | Introduction The automatic processing of continuous seismic data is important for monitoring earthquake, in which real data recorded by field stations located in different regions is transmitted to data cen- tre through internet or satellite communication systems. Automatic processing will run firstly on data, afterwards these automatic processing results will be reviewed and modified. The load of interactive analysis would be increase if there were more false events or missed events after run…
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| 关 键 词： | 地震 自动信号检测 支持向量机 模式识别 |
| 文章编号： | 1000-9116（2007）01-0088-10 |
| 收稿时间： | 2005-11-12 |
| 修稿时间： | 2006-12-06 |
Automatic signal detection based on support vector machine |
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| Wang Hai-jun and Liu Gui-zhong. Automatic signal detection based on support vector machine[J]. Acta Seismologica Sinica(English Edition), 2007, 20(1): 88-97. DOI: 10.1007/s11589-007-0088-x |
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| Authors: | Wang Hai-jun and Liu Gui-zhong |
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| Affiliation: | (1) Xi’an Jiaotong University, Xi’an, 710049, China;(2) Northwest Institute of Nuclear Technology, Xi’an, 710024, China |
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| Abstract: | Algorithm of STA/LTA is frequently used in automatic signal detection, in which the range of detection threshold is (0, ∞), the optimal threshold should be determined by experiment to make a balance between false detection and missing detection. By using the theory of pattern recognition, a new algorithm for automatic signal detection based on support vector machine was proposed and the method of preprocess and pattern feature extraction were discussed as well as the selection of kernel function for support vector machine. The detection performance of the new algorithm was analyzed by means of real seismic data. The experiments showed that the new method could simplify the selection of threshold and detect signal accurately. In addition to the better performance of anti-noise, the ratio of false detection could decrease 85% in comparison with that of STA/LTA. |
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| Keywords: | support vector machine earthquake automatic processing pattern recognition |
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