| 自相关法单频干扰识别与消除方法 |
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| 引用本文： | 高少武,赵波,祝树云,罗国安,贺振华.自相关法单频干扰识别与消除方法[J].地球物理学报,2011,54(3):854-861. |
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| 作者姓名： | 高少武 赵波 祝树云 罗国安 贺振华 |
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| 作者单位： | 1. 成都理工大学油气藏地质与开发工程国家重点实验室,成都 610059;
2. 中国石油集团东方地球物理公司物探技术研究中心,涿州 072750 |
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| 摘 要： | 常规单频干扰压制方法需要估算频率、振幅和相位,由于频率和相位与单频干扰呈非线性函数关系,因此任何参数估算方法都会非常费时,且计算效率低下.本文提出的自相关法单频干扰识别与消除方法,利用地震数据自相关函数计算单频干扰自相关函数,采用余弦函数自适应减算法快速估算单频干扰.本方法不需要估算单频干扰的频率、相位和振幅参数,因此...
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| 关 键 词： | 地震数据采集 单频干扰 信噪比 自适应减算法 自相关法 |
| 收稿时间： | 2010-03-12 |
Identification and elimination of monofrequency interference in seismic data by an autocorrelation algorithm |
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| GAO Shao-Wu,ZHAO Bo,ZHU Shu-Yun,LUO Guo-An,HE Zhen-Hua.Identification and elimination of monofrequency interference in seismic data by an autocorrelation algorithm[J].Chinese Journal of Geophysics,2011,54(3):854-861. |
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| Authors: | GAO Shao-Wu ZHAO Bo ZHU Shu-Yun LUO Guo-An HE Zhen-Hua |
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| Institution: | 1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China;
2. Geophysical Technique Research Center, BGP, CNPC, Zhuozhou 072750, China |
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| Abstract: | Traditional monofrequency interference (MFI) suppression methods need to estimate the frequency, phase and amplitude of the MFI. However, a MFI has a nonlinear relationship with the frequency and phase parameters, and any parameter estimation is time consuming, its computational efficiency is very low. In the autocorrelation algorithm (ACA) proposed in this paper, the autocorrelation function of the MFI is computed by seismic records. Following that, the MFI is quickly estimated by a cosine function adaptive subtraction algorithm. The proposed method need not estimate the frequency, phase and amplitude of the MFI, so the MFI can be high-efficiently estimated and eliminated. The most prominent advantage of the ACA is that only interference with MFI component can be quickly and effectively eliminated, and useful signals have not been harmed. Therefore, the signal-to-noise ratio is improved in the vicinity of a MFI frequency component. The synthetic and real data examples illustrate that the proposed method is feasible and effective. |
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| Keywords: | Seismic data acquisition Monofrequency interference Signal-to-noise ratio Cosine function adaptive subtraction algorithm Autocorrelation algorithm |
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