摘要
分辨率是影响地震资料解释结果的一个重要因素,地震信号分辨率低,将导致小断层、薄互层难以识别.为此,将匹配追踪算法与核主成分分析(KPCA)方法应用于地震资料处理,提出了基于匹配追踪与核主成分分析的地震信号高分辨率处理方法.首先,利用匹配追踪算法通过稀疏分解不断迭代得到地震信号的最有效信息;然后,将子波替换为宽带Ricker子波进行整形处理,有效压制子波旁瓣,提高地震资料分辨率;最后,用核主成分分析方法将原始地震信号非线性映射到高维空间,在高维空间内重建地震信号,消除冗余信息.实际资料应用表明,经所提方法处理后的地震信号,波形更清晰,细节更丰富,处理结果有利于断层识别、薄层刻画,为后续地质资料解释、储层预测提供数据基础.
Abstract
Resolution is an important factor affecting the interpretation of seismic data.Low seismic signal reso-lution can lead to difficulties in identifying small faults and thin interbeds.To address this issue,this study pro-poses a high-resolution seismic signal processing method based on a matching pursuit algorithm and kernel prin-cipal component analysis(KPCA).Firstly,the matching pursuit algorithm is utilized to iteratively obtain the most effective information on seismic signals through sparse decomposition.Next,the wavelet is replaced by a wideband Ricker wavelet for shaping processing,effectively suppressing the side lobes of the wavelet and im-proving the resolution of seismic data.Finally,the original seismic signals are mapped to a high-dimensional space through nonlinear mapping using KPCA,and the seismic signals are reconstructed in the high-dimen-sional space to eliminate redundant information.Practical applications demonstrate that the seismic signals pro-cessed by this method exhibit clearer waveforms and richer details,which are beneficial for fault identification and characterization of thin bed,thereby providing a data foundation for subsequent geological data interpreta-tion and reservoir prediction.
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