局部频率域SVD压制随机噪声方法

常规SVD技术去除随机噪声是在时间域进行的,对水平同相轴有较好的去噪效果;但对同相轴是倾斜或弯曲的情况,则要进行局部倾角扫描校正,从而限制了其在实际中的应用。为此,本文研究了局部频率域SVD压制随机噪声方法,有效克服了时间域局限性。首先对时空域滑动窗口内地震数据进行傅氏变换,并对每个频率切片构建Hankel矩阵,再对Hankel矩阵进行SVD滤波(降秩重构),最后反变换到时间域,得到去除随机噪声的结果。通过构建块Hankel矩阵,将该方法扩展到三维地震数据体的噪声压制处理中。模型及实际资料处理结果对比表明,该方法在有效压制随机噪声的同时,能够较好地保留有效信号,优于常规频域预测滤波结果。

Local SVD for random noise suppression of seismic data in frequency domain

Conventional SVD technology suppresses random noise in time domain,which is suitable for seismic data with flat events.But for dipping and curving events,SVD methods in time domain are greatly limited by the necessary requirement of automatic tracing slopes of events in practice.In this paper,local SVD filtering in frequency domain is described to avoid the above limitation.Seismic data within a local window sliding in space and time are first extracted and Fourier transformed.Then SVD filtering is applied to constant-frequency slices by forming the Hankel matrix.Once all frequencies within the signal bandwidth are noise reduced,the clean section is obtained by taking the inverse DFT of each trace.This method is also extended to remove random noise from stacked 3D seismic volumes by forming a Hankel matrix.Synthetic data and field data processing indicates that this method can suppress random noise more effectively and preserve signal simultaneously,and does much better than conventional prediction filtering methods in frequency domain.