“测量的本质就是信息获取”观点在测量理论研究领域已逐渐成为共识；直接应用香农信息理论的基本观点、方法来描述测量系统也引起一些研究者的兴趣。本论文就测量系统实现以信息获取为目的的工作内涵的转变问题，以及对测量系统在信息熵意义下的建模方法、性能分析和误差评价问题进行研究与讨论。论文的主要内容如下： 提出一种新的信息量，并对其相关性质进行证明，为串联形式的信息测量系统的表征起到了关键作用；由此得出的若干特性，为系统的分析与计算提供方便；另外，所得的熵平衡定理揭示了在信息获取过程中不同随机变量的熵对信息的加工与处理过程；以测量是被测信源不确定性由大到小的客观历程为基础，阐明测量过程中信息获取的熵减机制，总结测量有效性原则。这些研究为建立统一的测量系统理论奠定基础。 线性测量系统的熵描述是一个具有重要应用意义的问题，本文第三章基于高斯随机过程在Toeplitz分布条件下的时频关系引理，得到连续熵的频域表征，从而证明出线性网络熵定理，并且推导出级联线性网络表达式，文中对定理在应用上存在的频域与熵域关系不对应问题，给予形式化方法的解释，为在信息熵意义下分析和设计测量系统提供思路。 为了将基于信息熵的测量系统描述方法应用到具体测量系统的设计与优化中，本文首先探究了放大环节中熵增量与噪声熵的本质区别，以及带通滤波网络的信息获取特性；其次，分析量化过程的信息传输特性，研究不同概率分布条件下的输入信源对量化过程信息获取的影响；此外，分析信息预处理的熵模型，并讨论信息获取意义下影响算法性能的主要因素；最后，给出基于相对误差熵的测量结果评价指标。这些方法为测量系统的设计与优化提供理论支撑与技术指导。 针对核磁共振测井系统中若干信息获取最大化问题，本文利用基于熵的测量系统描述方法进行研究。(1)通过建立核磁共振弛豫过程的熵模型，定量确定第一个回波在不同弛豫条件下的信息比重，设计改进型的振铃噪声抑制方法，从而完成地层束缚流体信息的获取工作。(2)本文就核磁测井原始回波数据在压缩过程中信息损失严重的问题进行研究，设计基于最优量化的压缩方法，确保在同压缩比条件下信息被最大化获取。(3)针对正则化反演方法在选取正则化项等先验信息时缺少理论依据的问题，本文从先验信息的最大熵角度，设计基于最大熵正则化的二维核磁测井反演算法，实现反演运算的信息获取最大化。 关键词：信息熵，测量系统，熵平衡定理，核磁测井系统，信息获取最大化，最大熵正则化

 The viewpoint that information acquisition is the essence of measurement has gradually become a consensus in the field of measurement theory research. The direct application of the basic viewpoints and methods of Shannon's information theory to describe the measurement system has also attracted the interest of some researchers. In this thesis, the measurement system realizes the transformation of the work connotation for the purpose of information acquisition, and studies and discusses the modeling method, performance analysis and error evaluation of the measurement system in the sense of information entropy. The main contents of the paper are as follows. A new noun that represents amount of information is proposed and its related properties are proved, which plays a key role in the characterization of the series information measurement system. Several characteristics are obtained to facilitate the analysis and calculation of the system. The obtained entropy balance theorem reveals the process of processing the information of different random variables in the process of information acquisition. The measurement is based on the objective process of the measured source uncertainty from large to small, clarifying the measurement process. The entropy reduction mechanism of information acquisition is clarified. The principle of measurement effectiveness is summarized. These studies laid the foundation for the establishment of a unified measurement system theory. The entropy description of linear networks is an important application. The third chapter is based on the Topelitz distribution theorem to obtain the frequency domain representation of continuous entropy, which proves the linear network entropy theorem and derives the cascaded linear network expression. In this paper, the relationship between the frequency domain and the entropy domain in the application of the theorem does not correspond to the problem, and the interpretation of the formal method is given to provide an idea for analyzing and designing the linear measurement system in the sense of information entropy. This paper first explores the essential difference between entropy increment and noise entropy in the amplification process, and the band-pass filter network are described by entropy, in order to apply the information entropy-based measurement system model to the method design and optimization of specific measurement process. Secondly, the information characteristics of the quantization process are analyzed, and the influence of the input source on the information acquisition of the quantization process under different probability distribution conditions is studied. In addition, the entropy model of information preprocessing is analyzed, and the main factors affecting the performance of the algorithm under the meaning of information acquisition are discussed. Finally, the error evaluation of measurement results based on relative error entropy is given. These methods provide theoretical support and technical guidance for the design and optimization of measurement systems. Using the description method of measurement system based on entropy, this paper studies the maximization of information acquisition in NMR logging system. Firstly, by establishing the entropy model of NMR relaxation process, the information proportion of the first echo under different relaxation conditions is quantitatively determined, and an improved ring noise suppression method is designed to complete the acquisition of formation bound fluid information. Secondly, the problem of serious information loss in the compression process of original NMR logging echo data is studied, and the compression method based on optimal quantization is designed to ensure that the information is maximized under the same compression ratio. Finally, in view of the lack of theoretical basis for regularization inversion methods in selecting prior information such as regularization terms, this paper designs a two-dimensional NMR logging inversion algorithm based on maximum entropy regularization from the perspective of maximum entropy of prior information to maximize information acquisition of inversion operations. Keywords: Information entropy, Measurement system, Entropy balance theorem, NMR logging, Maximization of information acquisition, Maximum entropy regularization