摘要
Seismic data contain random noise interference and are affected by irregular subsampling. Presently, most of the data reconstruction methods are carried out separately from noise suppression. Moreover, most data reconstruction methods are not ideal for noisy data. In this paper, we choose the multiscale and multidirectional 2D curvelet transform to perform simultaneous data reconstruction and noise suppression of 3D seismic data. We introduce the POCS algorithm, the exponentially decreasing square root threshold, and soft threshold operator to interpolate the data at each time slice. A weighing strategy was introduced to reduce the reconstructed data noise. A 3D simultaneous data reconstruction and noise suppression method based on the curvelet transform was proposed. When compared with data reconstruction followed by denoizing and the Fourier transform, the proposed method is more robust and effective. The proposed method has important implications for data acquisition in complex areas and reconstructing missing traces.
野外地震数据包含各种随机噪声干扰且在空间方向常进行不规则欠采样,影响后续资料处理,存在数据重建和噪声压制问题,而大多数据重建方法只能独立进行,对于噪声压制则无能为力,对于含噪地震数据的重建效果不理想,起不到压制噪声的效果。为此本文选用多尺度多方向的二维曲波变换进行三维地震数据同时重建与噪声压制,在此过程中引入凸集投影算法(POCS),采用指数平方根衰减规律的阈值参数及软阈值算子对每个时间切片单独进行重建。在此基础上,引入加权因子策略,使得在的重建过程中减少噪声对重建结果的影响,最终实现了一种能够同时进行三维地震数据重建和噪声压制的方法。通过与先重建后去噪以及傅里叶变换处理方法的比较,表明了该方法效果显著,这对于指导复杂地区数据采集和缺失地震道重建方面具有重要的实用价值。
基金
sponsored by the National Natural Science Foundation of China(Nos.41304097 and 41664006)
the Natural Science Foundation of Jiangxi Province(No.20151BAB203044)
the China Scholarship Council(No.201508360061)
Distinguished Young Talent Foundation of Jiangxi Province(2017)
