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Least square smoothing algorithm and gauss decomposition spectral analysis method in spectral gamma ray logging

机译:谱伽马射线测井中的最小二乘平滑算法和高斯分解谱分析方法

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In the exploration of deep mineral resources, natural gamma spectrometer logging technology is a method which can realizes qualitative and quantitative analysis of deep underground radioactive elements. However, the low energy resolution of natural gamma ray spectrometry logging results in low reliability of distinguishing the radioactive element, therefore the study of spectral analysis method is particularly important. In this paper, the least square smoothing algorithm and the Gauss decomposition method are studied. The results show that the third order polynomial with 5 points smooth window has a good smoothing effect on the spectral data. The spectrum processed by using of the Gauss decomposition method is affected by the separation degree and the peak to height ratio. It is fast and accurate to choose the Gauss decomposition method to solve the spectral data when the separation degree of overlapping peaks is greater than 0.4, and the difference of peak heights are large. This paper provides the basis for the application and selection of the generally analytical method in the actual logging processing, which is of great significance.
机译:在深部矿产资源的勘探中,天然伽马能谱仪测井技术是一种能够对地下深部放射性元素进行定性和定量分析的方法。但是,自然伽马能谱测井的能量分辨率低,导致放射性元素鉴别的可靠性低,因此对光谱分析方法的研究尤为重要。本文研究了最小二乘平滑算法和高斯分解方法。结果表明,具有5点平滑窗口的三阶多项式对光谱数据具有良好的平滑效果。使用高斯分解方法处理的光谱受分离度和峰高比的影响。当重叠峰的分离度大于0.4,且峰高差较大时,选择高斯分解法求解光谱数据是快速,准确的。本文为一般分析方法在实际测井处理中的应用和选择提供了依据,具有重要的意义。

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