波谱学杂志 ›› 2007, Vol. 24 ›› Issue (3): 311-319.

• 研究论文 • 上一篇    下一篇

核磁共振二维谱反演

顾兆斌;刘卫   

  1. 中国科学院 渗流流体力学研究所核磁共振室,河北 廊坊 065007
  • 收稿日期:2006-11-23 修回日期:2007-01-30 出版日期:2007-09-05 发布日期:2009-12-05
  • 通讯作者: 顾兆斌

The Inversion of Two-dimensional NMR Map

GU Zhao-bin;LIU Wei
  

  1. Laboratory of NMR, Institute of Porous Flow and Fluid Mechanics, Chinese Academy of Sciences, Hebei 065007, China
  • Received:2006-11-23 Revised:2007-01-30 Online:2007-09-05 Published:2009-12-05
  • Contact: Gu Zhao-bin

摘要: 核磁共振二维谱包含扩散系数 D 和横向弛豫时间 T2 的信息,利用核磁共振仪来测量多孔介质中物质的信息,根据其弛豫时间和扩散系数的差别来区分不同物质;利用全局反演方法,提出了核磁共振二维谱反演的物理模型和数学模型;介绍了传统的奇异值分解(SVD)和改进的奇异值分解反演算法;采用改进的奇异值分解法对核磁共振二维谱进行反演,其反演算法具有计算速度快和二维谱分布连续等优点. 它适合于信噪比较高的数据反演,当原始数据信噪比SNR≥100时,可以对二维谱图进行定量分析;当60≤SNR<100时,可以对二维谱图进行定性分析. 核磁共振二维谱可以一次性直接区分油和水,为核磁共振测井提供了新的科学技术.

关键词: 核磁共振, 弛豫, 扩散, 二维谱, 改进的奇异值分解

Abstract: Two-dimensional (2D) nuclear magnetic resonance (NMR) map commonly refers to a plot of diffusion coefficient (D) vs. transverse relaxation time (T2), which is often used to characterize materials in porous media. The materials with different D and T2 can be differentiated by such a map. In this paper, we introduced the physical and mathematical models underlying the inversion of 2D NMR map with a global inversion method. The performances of the traditional singular value decomposition (SVD) method and a improved SVD method in inverting 2D NMR maps were compared. The improved SVD method was shown to have advantages such as faster calculation speed and continued distribution of 2D NMR map. The results suggest that the improved SVD method is especially suitable for inversion of data having high signal-to noise ratio (SNR). Quantitative analysis of 2D NMR map was shown to be feasible when the SNR of the original data was greater than 100, while only qualitative analysis was feasible when 60≤SNR<100. The results also showed that 2D NMR maps can be used to differentiate oil and water directly, suggesting that it may provide a promising new technology for NMR logging.

Key words: NMR logging, relaxation, diffusion, two-dimensional map, singular value decomposition