Singular value decomposition (SVD) is a useful method for random noise suppression in seismic data processing. A structure-oriented SVD (SOSVD) approach which incorporates structure prediction to the SVD filter is effcient in attenuating noise except distorting seismic events at faults and crossing points. A modified SOSVD approach using a weighted stack, called structure-oriented weighted SVD (SOWSVD), is proposed. In this approach, the SVD filter is used to attenuate noise for prediction traces of a primitive trace which are produced via the plane-wave prediction. A weighting function related to local similarity and distance between each prediction trace and the primitive trace is applied to the denoised prediction traces stacking. Both synthetic and field data examples suggest the SOWSVD performs better than the SOSVD in both suppressing random noise and preserving the information of the discontinuities for seismic data with crossing events and faults.

奇异值分解（SVD）是在地震数据处理中抑制随机噪声的一种有用方法。将结构预测结合到SVD滤波器中的面向结构的SVD（SOSVD）方法除了可以使断层和交叉点处的地震事件失真之外，还可以有效地衰减噪声。提出了一种改进的使用加权堆栈的SOSVD方法，称为面向结构的加权SVD（SOWSVD）。在这种方法中，SVD滤波器用于衰减通过平面波预测生成的原始轨迹的预测轨迹的噪声。与每个预测迹线和原始迹线之间的局部相似性和距离有关的加权函数被应用于去噪后的预测迹线堆叠。