基于多道卷积信号盲分离的多次波自适应相减方法

本文将多次波自适应相减问题表示为一个多道卷积信号的盲分离问题.利用2D卷积核来表示预测多次波和实际多次波之间的差异,并采用分离出的一次波信号的非高斯性最大化作为优化目标,我们提出一种基于多道卷积信号盲分离的多次波自适应相减算法.为了求解上述非线性优化问题,所提方法将其转化为一个迭代线性优化问题,采用迭代最小二乘方法加以实现.由于采用了多道卷积信号盲分离模型,所提方法能够适应预测和真实多次波之间在时间及空间上的变化.通过对简单模型数据、Pluto数据和实际数据进行处理,验证了所提算法的有效性.

Adaptive multiple subtraction based on multi-traces convolutional signal blind separation

This paper represents the adaptive multiple subtraction problem as a blind signal separation problem using multi-traces convolutional signal blind separation model.By expressing the difference between the predicted and true multiples using a 2D convolutional kernel,we propose an adaptive multiple subtraction method based on the multi-traces convolutional signal blind separation technique,which adopts maximization of the non-Gaussianity of the recovered primaries as the objective function.To solve the above non-linear optimization problem,we transfer it to an iterative linear one,which is realized by the iterative least squares algorithm.Taking advantage of the multi-traces convolutional signal blind separation model,the proposed method is applicable to the situation that there are differences in the time-space domain between the predicted and true multiples.Through the processing of the simple model data,the Pluto data and the real seismic data,the validity of the proposed method is demonstrated.