| 强随机噪声条件下时窗类型局部线性化对TFPF技术的影响 |
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| 引用本文： | 李月,林红波,杨宝俊,吴宁,李诗宽,高峘.强随机噪声条件下时窗类型局部线性化对TFPF技术的影响[J].地球物理学报,2009,52(7):1899-1906. |
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| 作者姓名： | 李月 林红波 杨宝俊 吴宁 李诗宽 高峘 |
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| 作者单位： | 1.吉林大学信息工程系，长春 130012 ;2.吉林大学地球物理系，长春 130026;3.吉林大学地质学系，长春 130061;4.长春理工大学通信工程系，长春 130022 |
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| 基金项目： | 国家自然科学基金，吉林省科技发展计划项目 |
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| 摘 要： | 为了更广泛地应用时频峰值滤波方法消减地震勘探记录中强随机噪声，本文比较详细地探讨了该方法在应用时需要处理的时窗选取、一个时窗内局部线性化等主要基础技术问题.经过时变时窗的仿真计算，运用多项指标比较，包括整体背景强弱、振幅谱、信噪比、均方误差、有效子波波峰波谷幅值、畸变程度，综合评价出（L0+Lx）时窗滤波的效果较理想.对于三角波这类简单类型的周期波，其时频峰值滤波效果与边线段平均曲率变化、时窗长度等条件有关，即三角波边线段平均曲率越大，滤波结果的均方误差越大；另外，边线段平均曲率增大时，选取的时窗长度有变小的趋势.总之，在地震勘探中应用时频峰值滤波方法消减强随机噪声时，一方面要合理地选取时窗参数，另一方面时窗参数又不能变化过大，以避免对有效子波波形产生畸变影响.
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| 关 键 词： | 时频峰值滤波 自适应时窗 局部线性化 地震勘探 强随机噪声 |
| 收稿时间： | 2008-7-21 |
| 修稿时间： | 2009-6-5 |
The influence of limited linearization of time window on TFPT under the strong noise background |
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| LI Yue,LIN Hong-Bo,YANG Bao-Jun,WU Ning,LI Shi-Kuan,GAO Heng.The influence of limited linearization of time window on TFPT under the strong noise background[J].Chinese Journal of Geophysics,2009,52(7):1899-1906. |
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| Authors: | LI Yue LIN Hong-Bo YANG Bao-Jun WU Ning LI Shi-Kuan GAO Heng |
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| Institution: | 1.Department of Information Engineering, Jilin University,Changchun 130012,China;2.Department of Geophysics, Jilin University,Changchun 130026,China;3.Department of Geology ,Jilin University,Changchun 130061,China;4.Department of Telecommunication Engineering, Changchun University of Science and Technology 130022,China |
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| Abstract: | Time-frequency peak filtering (TFPF) method is a staple technique for eliminating strong random noise in seismic exploration data. For applying these techniques extensively, this paper discussed the details of application of this method, such as choice of the length of time window, local linearization in one time window, etc. For the results of the simulation of time-varying time window TFPF, we compare many values, including the strength of global background, the amplitude of spectrum, signal-to-noise ratio, mean square error, the amplitude of peak/valley of valid wavelet, and the degree of the aberrance of wavelets, and then conclude synthetically that time-windowing filtering (L0+Lx) will get rather ideal effect. In the case of triangle waves, the filtering effect of TFPF is related to the average curvature of side line, the length of time window, etc. The bigger the average curvature of side line is, the bigger the mean square error is; moreover, increasing the average curvature of side line will cause a trend of decreasing in the chosen length of time window. In conclusion, applying TFPF method for eliminating random noise in seismic exploration, suitable parameter of time window should be chosen, on the other hand, the parameter of time window should not vary too much, so the waveform of valid wavelet is not distorted. |
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| Keywords: | Time-frequency peak filtering Adaptive length of the time window Local linearization Seismic exploration Strong random noise |
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