基于谐波分解恢复弱势信号的高分辨率处理技术

High‑Resolution Processing Technology for Restoring Weak Signals Based on Harmonic Decomposition

地震资料高分辨率处理是预测薄储集层的有效手段,高分辨率处理的主要目的是有效恢复地震高、低频信息和拓宽频带,同时保持资料的信噪比和保真度。基于谐波分解恢复弱势信号的高分辨率处理技术,以压缩小波变换为基础,根据信号谐波分量对地震高、低频弱势信号进行恢复。首先,基于有效频带内的地震信号,采用压缩小波变换将其分解为各基频信号,然后计算各基频信号的高次谐波与低次谐波,将计算出的高次谐波与低次谐波加入小波变换系数中,最后进行小波逆变换,即可恢复高、低频弱势信号。在实现过程中,仅仅在有效频带内估算基频信号,能较好地保持信号的信噪比。地震信号的小波变换系数与地层反射系数具有一致性,因此该技术具有较高的保真度,可以较好保持相对振幅关系。实际应用表明,在保持资料信噪比的同时,该技术能大幅拓宽地震资料的频带,处理后的地震剖面断点更清楚,分辨能力改善明显,能较好地识别6 000 m以深约40 m的薄储集层。

Improving the resolution of seismic data processing is an effective means for predicting thin reservoirs.The primary objective of high-resolution processing is to effectively recover high-and low-frequency information of seismic data,broaden frequency bandwidth,and maintain signal-to-noise ratio and fidelity of seismic data.Using the high-resolution processing technology for restoring weak signals through harmonic decomposition,and based on compressed wavelet transform,the high-and low-frequency weak seismic signals were re-stored according to harmonic components.Firstly,the seismic signals within effective frequency bands were decomposed into various base-band signals by using the compressed wavelet transform.Then,the high-order and low-order harmonics of each baseband signal were calcu-lated and added to the wavelet transform coefficients.Finally,inverse wavelet transform was performed to restore the high-and low-frequen-cy weak signals.In this process,only the baseband signals within the effective frequency band are estimated,which helps to maintain the signal-to-noise ratio.The wavelet transform coefficients of seismic signals are consistent with the stratum reflection coefficients,verifying that the technology has high fidelity and good relative amplitude preservation.The actual application of the high-resolution processing tech-nology shows that it can maintain the signal-to-noise ratio and significantly widen the seismic bandwidth,resulting in clearer seismic profile breakpoints,higher resolution,and better identification of thin reservoirs of about 40 m thick at the depth below 6000 m.