The Study Of Feasibility To Detect Low Signal In Gamma-Ray Spectra Using Wavelet Analysis

There are two aspects in Gamma-ray spectrometry. The first is to identify nuclides, and the second is to determine the quantity of the nuclides. The energy and area of a full-energy peak in a gamma spectrum have direct bearing upon these two questions. Identification and quantification of nuclides may become difficult in measurements involving very complicated gamma-spectra, or in the low-level measurements, where peaks are weak and backgrounds are strong.So it is of significance to develop analysis technology of low signal measurement in gamma spectrometry.There are many methods in low-level measurements of radioactivity. These methods focus mostly on hardware, for example changing the measurement condition, improving measurement technique and so on. The ability of detecting weak peak is improved by these methods. But typically, the equipment is large and expensive. With the development of computer technology,some algorithms imerged in software. And these methods are limited to the smoothing of gamma spectra.The wavelet analysis is a very new methods of data and signal processing method. It has appeared great live blood in these fields. But there has been seen few reports on its application in gamma-ray spectroscopy. We have studied the feasibility of applying this method to improve the ability of detecting weak peak, and the ways the method maybe implemented. Following are the main contents of my thesis:Firstly we introduce the importance of low-signal detection and the status in quo. Secondly we briefly introduce the technique of gamma-ray measurement, and give a full introduction of the characteristic of frequency domain of gamma spectrum and summarize its features to provide basis to our work. We discuss the merits and defects of traditional methods, and the characteristic quantities representing the ability of detecting weak peak in gamma spectrum. Thirdly wavelet theories is briefly introduced such as the definition of wavelet and the transform of wavelet, basic idea of multiresolution analysis and the quick algorithm of Mallat and so on. In the next chapter, we study ways of improving the ability of detecting low signal in gamma spectrometry with the method of adaptive threshold in wavelet analysis. Our work mainly include the basic idea of technique of wavelet analysis, the choice of wavelet, scale of transform and so on. We sum up the merits and defects of this method. Lastly the method of modulus maximum is applied in our work experimently. And we give comments of this method.