Magnetic Resonance Sounding Signal Reconstruction And Noise Suppression Algorithm Based On Compressed Sensing

Magnetic resonance sounding(MRS)technology is an advanced groundwater detection method that can directly and quantitative acquire groundwater information.It is also widely used in the advanced detection of disaster water sources such as landslides and mine water inrush.However,in practical applications,MRS detection uses natural geomagnetic fields,shielding measures cannot be taken,resulting in a large amount of interference information such as environmental noise and man-made noise being captured by the instrument system,which seriously affects the quality of the collected data and cannot achieve effective signals extraction.How to develop a method of combining data acquisition and signal processing,aiming to directly acquire MRS signals,reduce environmental noise,and achieve accurate detection of target water bodies under complex environmental interference is the face of magnetic resonance detection technology in practical applications major problems.Compressed sensing(CS),a kind of information sampling acquisition theory,through the sparse characteristics of the signal,using the redundant information of the data,a small amount of signal is obtained by random sampling discrete samples,and finally use a nonlinear reconstruction algorithm to recover the signal.In view of this,this paper proposes to carry out research on MRS signal reconstruction and noise suppression based on compressed sensing,which has important significance and practical value.Based on the characteristics of MRS signal and noise,this paper first analysis the characteristics of several commonly used sparse bases to determine the sparse decomposition of noisy MRS signal using K-SVD learning dictionary;Secondly,the simulation of MRS signal compressed sensing reconstruction and noise suppression algorithm under different noise was carried out,and the influence of parameters such as signal-to-noise ratio,measured data length,and relaxation time on the performance of the algorithm was discussed;Finally,the performance of the proposed algorithm is verified through measured data processing experiments and comparison with other algorithms.The main research results include the following:(1)Determine the three elements in the application of compressed sensing algorithm.By comparing and analyzing the characteristics of fourier transform(FFT),Discrete Cosine Transform(DCT)and Learning Dictionary(K-SVD),K-SVD learning dictionary is selected as the sparse transform base;By comparing Gaussian random matrix,Bernoulli random matrix and partial Hadamard matrix,the Gaussian random matrix has stronger randomness and better reconstruction effect is selected as the measurement matrix;By comparing the orthogonal matching tracking and base tracking algorithms,the orthogonal matching tracking algorithm with fast calculation speed and high reconstruction accuracy is selected for reconstruction.(2)Carry out simulation experiments of reconstruction and noise suppression algorithms based on compressed sensing.In the analysis of the reconstruction effect,the simulation and analysis of the noiseless and noisy MRS signals are used to achieve a better signal reconstruction;Using RMSE and similarity coefficient as evaluation indicators,the reconstruction effect analysis under different signal-to-noise ratio and different measurement data lengths is carried out,which proves the effectiveness and universality of the algorithm in this paper,and can be based on the reconstruction error RMSE = 14.65 n V and related coefficient is 0.9978 to select the appropriate number of points.In the analysis of the noise suppression effect,the pre-processed noise-free MRS signal estimate is used as the initial value of the K-SVD dictionary,so that the signal and noise have different sparse characteristics on the sparse basis,and the signal-noise separation is achieved to the purpose of noise suppression.By analyzing different measurement data lengths,different signal-to-noise ratios and different relaxation time noise suppression effects,the effectiveness of the algorithm for noise suppression is proved,and the signal-to-noise ratio can be improved up to 23 d B.(3)Conducted field measurement data collection and processing experiments based on compressed sensing.After processing by the compressed sensing method,the signal-to-noise ratio can be improved by up to 15 d B.The signal processed by this method is compared with the results of other methods to verify the effectiveness of this method.