基于形态小波的核磁共振测井信号去噪及现场应用研究

核磁共振测井中采集到的回波串信号十分微弱,而背景噪声很强,使信噪分离困难。为解决这一问题,引入了结合数学形态学的特征识别和小波分解的多分辨率分析特性的形态小波方法。讨论了方法的数据基础和应用步骤,并与小波软阈值方法处理结果进行了对比分析。实测数据处理结果表明:形态小波去噪方法具有良好的细节保留和抗噪声能力,去噪效果优于小波软阈值滤波方法;在消除测井信号随机噪声的同时,能很好地保留信号的波形和特征,在较低信噪比下仍可有效地提取测井信号的有用信息,提高了T2谱的反演精度。

De-noising of NMR logging signals based on the morphological wavelet and its real application

In nuclear magnetic resonance (NMR) logging, the echo signal is very weak while the background noise is strong. Thus it is difficult to separate signal and noise in the data. To solve this problem, this work combined mathematical morphology and multi-resolution analysis of wavelet decomposition to generate a new de-noising method for NMR logging signals, called the morphological wavelet. This paper presents its principle and operation steps, and compares its de-noising effect with that of the wavelet soft-threshold method. The processing results of real logging data show that this new method can preserve details and resist against noise well, superior to the wavelet soft-threshold method. When it removes random noise, the signal waveforms and characteristics can be retained. Even in the case of low SNR, the useful information also can be effectively extracted, which enhances the inversion precision of the T2 spectrum.