基于3D匹配滤波器和伪地震数据算法的多次波自适应相减方法

多次波自适应相减是预测减去法压制多次波的关键步骤。为进一步去除残余多次波，基于常规2D匹配滤波方法，文中引入3D匹配滤波器，同时利用多个预测多次波道集以匹配原始数据。针对3D匹配滤波器可能造成的一次波损伤现象，利用相同的3D匹配滤波器同时拟合多个原始数据道集；同时，引入伪地震数据算法求解对一次波施加Huber范数最小化约束的优化问题，不需满足一次波与多次波正交的假设，能有效分离一次波与多次波。另外，在整个迭代过程中，伪地震数据算法只需利用Cholesky分解算法进行一次矩阵分解，计算效率较高。模型和实际数据的处理结果表明，与基于一次波能量最小化的3D匹配滤波器方法和基于伪地震数据算法的2D匹配滤波器方法相比，所提方法能更好地均衡一次波保护与多次波分离。

Adaptive multiple subtraction based on a 3D mat-ching filter and pseudo seismic data algorithm

Adaptive multiple subtraction is an important step for suppressing multiples by multiple prediction and subtraction.A 3D matching filter is proposed over a 2D matching filter to reduce residual multiples by combining with several gathers of the predicted multiples to match with original data.To avoid possible primary distortion,the same 3D matching filter is used to fit several original gathers.In addition,we introduce the pseudo seismic data algorithm to solve the optimization problem with the Huber norm minimization constraint on primaries.Without assuming the orthogonality of primaries and multiples,the new method can separate primaries from multiples effectively.During iterating,the pseudo seismic data algorithm only needs to conduct matrix decomposition with Cholesky factorization once,and the computational efficiency is high.Synthetic and field data have proved that the new method can better preserve primaries and remove multiples than a 3D matching filter based on minimization of primary energy and a 2D matching filter based on pseudo seismic data algorithm.