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对于岩性复杂地质条件,核磁共振(Nuclear Magnetic Resonance,NMR)相比其他测井方法能够提供更加丰富、有效的地质信息。本文针对核磁共振测井自旋回波信号带有大量噪声、影响反演计算和地层解释问题,提出一种集总自适应时变滤波分解的自旋回波信号去噪新方法,该方法将自适应时变滤波分解(Adaptive Time Varying Filter Decomposition,ATVFD)融合嵌入集总经验模分解(Ensemble Empirical Mode Decomposition,EEMD)算法中,避免了经验模态分解(Empirical Mode Decomposition,EMD)存在的边界效应和模态混叠问题,同时继承其自适应、后验的去噪优势。实验分析表明,该方法可有效地滤除回波信号噪声,得到的回波信号反演计算出的T2谱和孔隙度与岩心在实验室条件下测量一致,去噪效果明显。
For lithologic complex geological conditions, nuclear magnetic resonance (NMR) provides more abundant and effective geological information than other logging methods. In this paper, we present a new method for noise reduction of spin echo signals based on the lumped time-varying filtering decomposition of a nuclear resonance logging spin echo signal with a large amount of noise, affecting the inversion calculation and formation interpretation. This method adapts Adaptive Time Varying Filter Decomposition (ATVFD) fusion embeds the EEMD algorithm, which avoids the boundary effect and modulo of the empirical mode decomposition (EMD) State aliasing problem, while inheriting its adaptive, posterior denoising advantage. Experimental results show that this method can effectively filter out the echo signal noise. The calculated T2 spectrum and the porosity calculated by the echo signal are consistent with those measured under laboratory conditions, and the denoising effect is obvious.