基于曲线矢量空间的二维时频峰值滤波消减地震勘探随机噪声策略

项目名称： 基于曲线矢量空间的二维时频峰值滤波消减地震勘探随机噪声策略

项目编号： No.41274118

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 林红波

作者单位： 吉林大学

项目金额： 80万元

中文摘要： 面对能源地震勘探高信噪比、高分辨率、高准确度要求、复杂地表和近地表地质条件、以及赋存空间越来越复杂的勘探目标，尽可能多的消除随机噪声，高保真恢复有效信号（反射波），提高地震勘探记录信噪比,显得尤为重要。本申请项目拟研发一种利用多道地震记录横向空间相干特性的曲线空间二维时频峰值滤波技术，在消减复杂地震噪声的同时切实保真有效地震信号。通过分析地震同相轴局部走向，构建曲线矢量空间，从而增强矢量空间中有效信号的线性特征，同时保留复杂同相轴弯曲特征。这种基于曲线矢量空间的二维时频峰值滤波方法具有改善一维时频峰值滤波无偏估计的能力，从而达到滤波信号的保真。该技术研究随机噪声非平稳、非高斯、准相似性等复杂特性与二维时频峰值滤波参数之间的作用关系，构建时变加空变的二维时频峰值滤波技术，揭示随机噪声二维时频特征变化、时频分布互项干扰和一致性估计等关键问题，为油气资源探查中复杂强随机噪声消减提供可靠的处理手段

中文关键词： 地震勘探；复杂随机噪声压制；时空时频峰值滤波；自适应参数；时频分析

英文摘要： It plays an important role in petroleum seismic to remove the seismic random noise as much as possible and recover the valid signal (reflection wave) with high fidelity, meeting the requirements of the high signal-to-noise ratio (SNR), the high resolution and the high accuracy under the complex surface and near surface situation for the increasingly complex seismic objects in cumulated reservoir. The two-dimensional time-frequency peak filtering in curve vector space method will be developed to attenuate the seismic noise and preserve the seismic signal accurately by exploring seismic lateral coherence in this proposal. This method constructs the curve vector space according to the local direction of the event. Therefore the linearity of the signal in vector space is improved and the curve information of the event is preserved. Consequently, this two-dimensional time-frequency peak filtering method based on the vector space enhances the unbiased filtering ability of time-frequency peak filtering to obtain the accurate filtered signal. Furthermore, we will study the relationship between the filtering parameters and the characteristics of the seismic noise, including nonstationarity, nonlinearity and quasi-similarity, etc. Then the temporal and spatial various two-dimensional time-frequency peak filtering method w

英文关键词： seismic exploration；complicated random noise suppression；spatio-temporal time-frequency peak filtering；adaptive parameter；time-frequency analysis