| 小样本地震信号识别研究 |
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| 引用本文： | 范晓易,王夫运,鄢兆伦,李婷婷,周康雅,王 丽.小样本地震信号识别研究[J].大地测量与地球动力学,2022,42(11):1207-1210. |
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| 作者姓名： | 范晓易 王夫运 鄢兆伦 李婷婷 周康雅 王 丽 |
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| 摘 要： | 研究支持向量机方法在小样本地震信号识别方面的可行性，结果表明，随着样本量增加，该方法的识别率出现先升高再降低的现象。使用山东和江苏地区2006~2017年的地震数据进行实验，仅需每类30个训练样本即可达到85%左右的识别率。该方法识别率的提高不依赖于大量样本的加入，不仅适合于地震数据样本量少的地区开展地震信号识别研究，而且为精简样本库、降低运行成本提供了新思路。
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| 关 键 词： | 地震信号识别 小样本 支持向量机 特征向量 |
Research on Small Sample Seismic Signal Recognition |
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| FAN Xiaoyi,WANG Fuyun,YAN Zhaolun,LI Tingting,ZHOU Kangya,WANG Li.Research on Small Sample Seismic Signal Recognition[J].Journal of Geodesy and Geodynamics,2022,42(11):1207-1210. |
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| Authors: | FAN Xiaoyi WANG Fuyun YAN Zhaolun LI Tingting ZHOU Kangya WANG Li |
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| Abstract: | In this paper, we study the feasibility of support vector machine in small sample seismic signal recognition. The results show that with the increase of sample size, the recognition rate of this method increases first and then decreases. Using the seismic data of Shandong and Jiangsu in 2006-2017, only 30 training samples per class can achieve the correct recognition rate of about 85%. The improvement of recognition rate does not depend on the addition of a large number of samples. It is not only suitable for the study of seismic signal recognition in areas with few seismic data samples, but also provides a new idea for simplifying the sample library and reducing the operation cost. |
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| Keywords: | seismic signal recognition small sample support vector machine feature vector |
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