| As the core part of field neutron logging system,neutron signal transmission and reconstruction processing will directly affect the analysis and study of nuclear pulse signal.Under the condition of pursuing high counting rate,compressive sensing is better than Nyquist sampling theorem for signal transmission and reconstruction processing.Research on neutron signal transmission reconstruction based on adaptive compressed sensing will obviously optimize the neutron signal performance in field logging communication speed and reconstructed SNR.The thesis mainly completed the following work:(1)Completed the existing internal rules of the neutron signal analysis of nuclear logging tools.By compressing sensing framework implements the sparse representation of nuclear signals.The neutron signal is analyzed in a variety of transformation of experimental results,to determine the best sym4 level6 sparse matrix.(2)The Gaussian convolution algorithm is introduced to improve the amplitude resolution of the neutron coefficient,and the maximum curve transformation rate of the threshold-the sparsity curve is used as adaptive coefficient threshold.The adaptive sparse coefficient contraction matrix is designed,the adaptive effective sparse basis is constructed,and the sparse coefficient is optimized.(3)Monte Carlo sampling nonlinear transformation is used to generate deterministic Gaussian pseudo-random Numbers observation matrix.The position of the known support set is determined based on the prior information of the optimal curve rate of change of neutron signal,and the adaptive observation matrix is constructed.So as to optimize the number of observations.The paper mainly has the following two aspects of innovation:(1)The adaptive sparse coefficient contraction matrix is designed to optimize the coefficient and speed up the neutron signal transmission.The Gaussian convolution algorithm is introduced to improve the resolution of the neutron sparse amplitude,and the threshold-the sparsity maximum curvature as adaptive coefficient threshold.The adaptive sparse coefficient contraction matrix is designed,the adaptive effective sparse basis is constructed,and the coefficient sparsity is optimized.(2)An adaptive observation matrix based on deterministic monte carlo pseudo-random Numbers and prior information of neutron signal is constructed,which optimizes the observation matrix and speeds up the neutron signal reconstruction.Based on the prior information of neutron signal the threshold value-the sparsity optimal curvature,the known support set position is determined,and the observation matrix is partitioned and shrunk,the deterministic pseudo-random adaptive observation matrix is constructed,and the observation times are optimized.The experimental results show that the adaptive compressive sensing reconstruction of neutron signal improves the reconstructed SNR and has better capability of well logging transmission and processing.adaptive sparse matrix improved neutron coefficient selection problem,the adaptive observation matrix to reduce the logging data sampling time,reduces the neutron communication system transmission storage cost. |
