基于高效字典学习算法的地震数据去噪  

Seismic Data Denoising Based on Efficient Dictionary Learning Algorithm

作　　者：叶宇晗 何宗斌[1] YE Yu-han;HE Zong-bin(College of Geophysics and Petroleum Resources,Yangtze University,Wuhan 430000,China)

机构地区：[1]长江大学地球物理与石油资源学院,武汉430000

出　　处：《科学技术与工程》2024年第25期10677-10687,共11页Science Technology and Engineering

基　　金：中国石油科技创新基金(2022DQ02-0301)。

摘　　要：地震数据去噪在地震资料处理中扮演着关键的角色,提升地震数据的信噪比将为后续高质量处理和精确解释奠定坚实基础。目前,地震数据去噪的方法已经得到了广泛发展,其中字典学习方法具有独特的优势。当前经典的K-奇异值分解(K-singular value decomposition,K-SVD)字典学习算法存在去噪结果损失了部分原始信号和计算效率不太理想等问题,为了将这些问题进一步优化,提出了一种基于顺序广义K-均值算法(sequential generalized K-means,SGK)的字典学习方法用于地震数据去噪。首先,从样本数据中提取随机位置的块,并移除空白块,以初始化字典。接着,在字典学习阶段,通过地震数据本身的特征自适应地构造出最新的稀疏表示字典。随后,利用学得的字典对包含噪声的地震数据分块进行去噪处理,将去噪后的块进行平均处理,并重新构建图像块,最终实现地震数据的去噪。通过合成数据和实际数据的实验,从信噪比、计算效率以及对有效信号的保护方面验证本文方法的去噪性能。In seismic data processing,the denoising of earthquake data plays a crucial role,with the enhancement of the signal-to-noise ratio serving as a foundational step for subsequent high-quality processing and precise interpretation.Presently,methods for seismic data denoising have undergone extensive development,with dictionary learning methods demonstrating unique advantages.The current classical K-singular value decomposition(K-SVD)dictionary learning algorithm exhibits issues such as partial loss of the original signal in denoising results and less-than-ideal computational efficiency.In order to further optimize these problems,a dictionary learning method based on the sequential generalized K-means(SGK)algorithm for seismic data denoising was proposed.Initially,blocks of random positions were extracted from the sample data,with blank blocks being removed to initialize the dictionary.Subsequently,in the dictionary learning phase,the most recent sparse representation dictionary was adaptively constructed based on the inherent features of seismic data.Following this,the learned dictionary was employed to denoise seismic data blocks containing noise.The denoised blocks were then subjected to averaging,and the image blocks were reconstructed,ultimately achieving the denoising of seismic data.The denoising performance of the proposed method was validated through experiments using synthetic and real seismic data.The evaluation encompasses aspects such as signal-to-noise ratio,computational efficiency,and the preservation of effective signals.

关 键 词：压缩感知 字典学习 SGK 地震数据去噪 

分 类 号：P631[天文地球—地质矿产勘探]