【摘要】 Radon变换的分辨率是其进行地震数据处理的关键因素。基于Bayes反演理论的迭代加权方法改善了Radon变换的分辨率,但其收敛速度较慢;由于Radon变换空间的强相关性,常用的迭代软阈值方法(ISTA)应用于Radon变换反演时的收敛速度也较慢,且分辨率较低。为此,将迭代加权最小二乘法嵌入ISTA中,形成了加权ISTA算法。该方法引入高分辨Radon变换中加权矩阵的思想,利用Radon参数的先验信息约束反演误差函数,克服了ISTA收敛速度慢、分辨率低的缺点。合成记录和实际地震资料处理结果表明,该方法提高了Radon变换分辨率,在多次波分离、噪声压制中效果显著。更多还原

【Abstract】 The resolution of Radon transform is the key to seismic data processing.The iterative weighting method based on Bayes inversion improves the resolution of Radon transform,but its convergence rate is low.In light of the strong correlation between Radon transform spaces,the convergence rate of the iterative soft threshold algorithm applied to Radon transform inversion is also low,and the resolution is poor.In this paper,the iterative reweighted least squares algorithm is embedded into the iterative soft threshold algorithm to form a reweighted-iterative soft threshold algorithm.The idea of weighted matrix in high-resolution Radon transform is introduced,and the prior information of Radon parameters is employed to constrain the inversion error function,overcoming the disadvantages of slow convergence and low resolution of the iterative soft threshold algorithm.Synthetic records and real seismic data processing show that this method improves the resolution of Radon transform and achieves good performance in multiple separation and noise suppression.更多还原