Magnetic resonance sounding signal detection based on statistical noise reduction method of Bayes Bootstrap

作者：Zhang Hairu^*; Ouyang Shan; Wang Guofu; Zhang Faquan

来源：Journal of Central South University(Science and Technology), 2014, 45(9): 3144-3149.

• Bootstrap model
• Feature parameters
• Linear relationships
• Magnetic resonance sounding (MRS)
• Magnetic resonance soundings
• Reconstruction accuracy
• Statistical errors
• Statistical noise

In order to extract a set of feature parameters of magnetic resonance sounding (MRS) signal, an algorithm of statistical noise reduction was presented over layered conductive earth, which took full use of the linear relationship aong the logarithmic MRS signal of each layered electrical conductive media and its sampling time. It estimated two optimal points from the real inspected signal sequence to reconstruct the MRS signal. Since it used only two optimal statistical points instead of the set of parameters with the minimum global statistical error, the algorithm could enhance the reconstruction accuracy of the MRS signal. The algorithm translated the multi-period MRS signal of each layered electrical conductive media sequence into two-dimensional matrix at first. Then, it structured the Bayes Bootstrap model to estimate the error of each column vector. Finally, according to these results of error analysis, the two optimal points were acquired. The results show the algorithm has a much lower average error rate than the traditional ones. It can extract effective feature parameters from the acquired MSR signal under poor SNR (1 dB) at the average error rate 5.5%. Furthermore, it also works for complicated original MSR signal, which contains the stable stochastic noise, the power frequency disturbing signal and the spike noise. With it the time for the sample period can be reduced greatly, inversion precision of the MRS systems can be improved.

• 出版日期2014
• 单位西安电子科技大学