Open AccessSpectral data processing based on maximum noise fraction
Author(s) -
Wentai Guo,
Liangquan Ge,
Fēi Li,
Chuanhao Hu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012028
Subject(s) - principal component analysis , noise (video) , noise reduction , data processing , computer science , data set , transformation (genetics) , pattern recognition (psychology) , algorithm , artificial intelligence , biochemistry , chemistry , image (mathematics) , gene , operating system
The processing of gamma spectrum measurement data has always been an important step in data interpretation of the survey area. It includes the correction of radioactivity interference caused by non-measuring targets, the correction of effects caused by different heights or topography in the survey environment, the smoothness and denoising of the measurement spectrum, etc. The accuracy of each step will affect the final data resources. The interpretation of the materials results in inaccurate regional radioactivity assessment. Maximum noise separation transformation is a similar method to principal component transformation, which is used to reduce dimensionality and noise of data. This paper takes the noise reduction process of gamma spectrum data processing as the research direction, uses SPSS data processing software, combines with MATLAB to denoise the data, and uses the maximum noise separation transformation as the denoising method, applies it to denoise the spectral data, and uses the principal component analysis method and the maximum noise fraction method to denoise the same set of data separately. After analysis, it is concluded that the maximum noise fraction method is superior to principal component analysis when there is a large noise variance for a set of data.