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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >An Efficient Method for NMR Data Compression Based on Fast Singular Value Decomposition
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An Efficient Method for NMR Data Compression Based on Fast Singular Value Decomposition

机译：基于快速奇异值分解的NMR数据压缩有效方法

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摘要

To improve the processing speed of nuclear magnetic resonance (NMR) echo data, data compression is essential prior to NMR inversion due to the large amount of raw echo data acquired via NMR logging. In this letter, a fast singular value decomposition (FSVD) method is proposed to compress NMR data, which differs from the SVD method by developing a lower dimensional submatrix that can capture mast action in the kernel matrix based on a random Hadamard matrix, and then decomposing the sub-matrix using SVD. The 2-D NMR relaxation data are taken as examples to evaluate the efficiency of the FSVD method. The inverted T-1-T-2 spectra after FSVD compression are compared with spectra after SVD compression and spectra without compression through numerical simulation experiments. Results show that under the same conditions, the compression time is shorter for the FSVD method than for the SVD method, the inversion time is far shorter for compressed NMR data than for uncompressed NMR data, and the accuracy of the inverted T-1-T-2 spectra after compression is close to that without compression. In addition, the effect of the Hadamard matrix on the accuracy and speed of the FSVD method is studied through 1000 random simulations. Findings show that the compression results of the FSVD method with different Hadamard matrices are close, indicating that the efficiency of this method is not affected by the Hadamard matrix.
机译：为了提高核磁共振（NMR）回波数据的处理速度，由于要通过NMR测井采集大量原始回波数据，因此在NMR反转之前必须进行数据压缩。在这封信中，提出了一种快速奇异值分解（FSVD）方法来压缩NMR数据，该方法不同于SVD方法，它开发了一个低维子矩阵，该矩阵可以基于随机Hadamard矩阵捕获内核矩阵中的桅杆动作，然后使用SVD分解子矩阵。以2-D NMR弛豫数据为例来评估FSVD方法的效率。通过数值模拟实验，将FSVD压缩后的倒T-1-T-2光谱与SVD压缩后的光谱和未压缩的光谱进行了比较。结果表明，在相同条件下，FSVD方法的压缩时间比SVD方法短，压缩NMR数据的反演时间比未压缩NMR数据的反演时间短得多，倒置T-1-T的准确性压缩后的-2光谱接近于未压缩的光谱。此外，还通过1000次随机模拟研究了Hadamard矩阵对FSVD方法的准确性和速度的影响。结果表明，采用不同Hadamard矩阵的FSVD方法的压缩结果接近，这表明该方法的效率不受Hadamard矩阵的影响。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2019年第2期|301-305|共5页
  • 作者

    Guo Jiangfeng; Xie Ranhong; Jin Guowen;

  • 作者单位

    China Univ Petr, State Key Lab Petr Resources & Prospecting, Beijing 102249, Peoples R China|China Univ Petr, Key Lab Earth Prospecting & Informat Technol, Beijing 102249, Peoples R China;

    China Univ Petr, State Key Lab Petr Resources & Prospecting, Beijing 102249, Peoples R China|China Univ Petr, Key Lab Earth Prospecting & Informat Technol, Beijing 102249, Peoples R China;

    China Univ Petr, State Key Lab Petr Resources & Prospecting, Beijing 102249, Peoples R China|China Univ Petr, Key Lab Earth Prospecting & Informat Technol, Beijing 102249, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data compression; fast singular value decomposition (FSVD); Hadamard matrix; nuclear magnetic resonance (NMR); T-1-T-2 spectra;

    机译：数据压缩;快速奇异值分解（FSVD）;Hadamard矩阵;核磁共振（NMR）;T-1-T-2光谱;

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