基于指数阈值迭代法的高精度地震数据重建

High Precision Reconstruction of Seismic Data Based on Exponential Threshold Iteration Method

地震勘探数据采集中,地震道缺失是不可能避免的现象。为了满足后续处理和解释的要求,缺失道数据重建是地震勘探数据处理中必不可少的预处理环节。为此,提出一种基于指数阈值迭代法的高精度重建方法进行叠前重建。引入能够刻画地震数据局部化特征的多尺度多方向二维曲波变换,采用阈值迭代法进行数据重建,并在迭代过程中采用软阈值算子去除由欠采样所引起的随机噪声干扰。同时在重建过程中针对传统阈值参数收敛速度较慢的缺点,提出了一种新的指数阈值参数公式,降低计算迭代次数和提高重建精度。理论数据的模拟表明,该方法重建效果显著,计算速度较快,应用于实际地震勘探资料,获得较好的重建效果。

During the data acquisition of seismic exploration, the phenomenon of missing traces are unavoidable. In order to meet the requirements for subsequent processing and interpretation, missing data reconstruction is essential preprocessing steps in any seismic data processing chain. The data reconstruction method based on the exponential threshold iterative method has been introduced in the paper. Firstly, multi-scale and multi-directional curvelet transform to characterize the local features of seismic data has been introduced and the threshold iterative method is adopted. Meanwhile, a soft thresholding is introduced to remove the random noise arisen by the under- sampling. Aiming at the disadvantage of slow convergence in traditional threshold parameter during the reconstruction process, an new exponential decreased threshold is also proposed and it can reduce iterations and improve reconstruction efficiency. The simulation results on synthetic seismic data showed that this method has a better effect and faster computation. At last, we apply this technology into real seismic data and obtain a good result.