论文详情
为了降低地震数据采集成本、提高采集效率并保持数据规则和完整, 首先, 针对随机采集观测系统不规则的问题, 提出了观测系统规则投影的技术流程, 填补缺失的炮点和检波点信息, 然后, 在压缩感知的框架下, 利用字典学习与稀疏表示进行三维地震数据重建。对提出的字典学习方法, 利用批量正交匹配追踪避免直接对矩阵求逆造成的计算量大的问题, 利用交替最小二乘代替奇异值分解提高计算效率, 同时对稀疏系数进行阻尼约束, 避免对噪声的拟合从而得到更好的字典。针对常规时间域字典学习地震数据重建方法存在计算效率低、弱信号保护能力差等问题, 在频率域进行地震数据重建, 对有效信号所在频带范围进行处理, 有效减少计算量、压制噪声、提高重建结果的信噪比, 形成了针对地震数据随机采集的观测系统规则投影、地震数据重建技术流程。实际资料应用结果表明, 通过规则观测系统投影、地震数据重建有效提升了叠前地震资料品质, 获得了较好的成像效果。
To reduce the cost of seismic acquisition, improve the acquisition efficiency and maintain the regularity and completeness of seismic data, the regular projection of geometry is proposed to fill missing shots and receivers for random seismic data acquisition.Under the framework of compressive sensing, dictionary learning and sparse representation are used to reconstruct seismic data.Compared with traditional dictionary learning method, the proposed method replaces orthogonal matching pursuit (OMP) with batch-OMP to avoid heavy computation in direct inversion of matrix, and also uses alternating least squares (ALS) to take place of singular value decomposition (SVD) to improve computation efficiency.Moreover, to avoid fitting noise, a damped constrain is applied to sparse coefficients for obtaining better dictionary atoms.Frequency domain dictionary and seismic data reconstruction are proposed to tackle the issues of low computational efficiency and poor capability of protecting weak signal in the time domain using traditional dictionary learning methods.Only seismic data in principal frequency band is used to reconstruct seismic data, which can effectively reduce computation workload, suppress noise and improve signal-to-noise ratio.Thus, the technical process of regular projection of geometry and seismic data reconstruction for random acquisition of seismic data is formed.The proposed method is applied to field data, demonstrating that the quality of prestack seismic data is effectively improved through regular projection of geometry and seismic data reconstruction, contributing to better imaging results.