| Ricker子波核最小二乘支持向量回归滤波方法的稳健性研究 |
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| 引用本文： | 邓小英,刘涛,罗勇.Ricker子波核最小二乘支持向量回归滤波方法的稳健性研究[J].地球物理学报,2011,54(3):845-853. |
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| 作者姓名： | 邓小英 刘涛 罗勇 |
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| 作者单位： | 1. 北京理工大学电子工程系,北京 100081;
2. 中国人民解放军驻航天科工集团二院军事代表室, 北京 100854;
3. 中国人民解放军驻中国电子信息产业集团12院军事代表室, 北京 100846 |
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| 摘 要： | 除了信噪比、有效子波畸变等,稳健性(Robustness)也是度量滤波方法效果的一个重要的物理量,它刻画了滤波系统应对异常点值的能力.一般用影响函数作为评价稳健性的工具.支持向量机方法已较成功地应用于信号与图像的滤波中,尤其Ricker子波核方法更适于地震勘探信号处理.通过考察Ricker子波核最小二乘支持向量回归(L...
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| 关 键 词： | 支持向量回归 稳健性 影响函数 权函数 地震勘探资料 |
| 收稿时间： | 2010-08-31 |
Robustness of least squares support vector regression filtering method with Ricker wavelet kernel |
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| DENG Xiao-Ying,LIU Tao,LUO Yong.Robustness of least squares support vector regression filtering method with Ricker wavelet kernel[J].Chinese Journal of Geophysics,2011,54(3):845-853. |
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| Authors: | DENG Xiao-Ying LIU Tao LUO Yong |
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| Institution: | 1. Department of Electronic Engineering, Beijing Institute of Technology, Beijing 100081, China;
2. Military Representative Office of PLA in Second Research Institute of CASIC, Beijing 100854, China;
3. Military Representative Office of PLA in Twelfth Research Institute of CEC, Beijing 100846, China |
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| Abstract: | Besides the signal-to-noise ratio and distortion of desired wavelets, the robustness is also an important physical quantity to measure the effect of a filtering method. The robustness expresses how a filtering system to deal with outliers. Generally, the influence function is used as a tool to assess the robustness of methods. Support vector machine has been successfully applied to the filtering of signal or image. Especially, the Ricker wavelet kernel method is suitable for the seismic data processing. It can be proved by checking the influence function of least squares support vector regression (LS-SVR) with the Ricker wavelet kernel that the robustness of this method is less satisfactory. In this paper the weighted method is used to improve the robustness of LS-SVR with the Ricker wavelet kernel. From many theoretical experiments, we obtain an improved weight function. After using the weight function, the robustness is quite satisfactory. Furthermore, we apply the weighted LS-SVR with the Ricker wavelet kernel to process the noisy synthetic and real seismic data. As a result, the good performance is achieved. Considering that the influence function of the LS-SVR system with a square loss function is not bounded, the weight function proposed can be effectively applied to the processing with similar loss function such as denoising, signal detecting, resolution improving, predicting, etc. |
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| Keywords: | Support vector regression Robustness Influence function Weight function Seismic data |
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