Gamma nuclide identification method

Abstract

The invention provides a gamma nuclide fast identification method in a complex radiation field, comprising the following steps: measuring a radiation field to get a gamma energy spectrum; deducting a comprehensive background from the gamma energy spectrum and reducing the noise of the gamma energy spectrum to get a net energy spectrum; determining potential nuclides according to the peak positions in the net energy spectrum; calculating the total net peak area of each potential nuclide; standardizing the total net peak area of each potential nuclide to get the standardized total net peak area of the potential nuclide; deducting a Compton scattering background from the standardized total net peak area of each potential nuclide to get the pure peak area value of each potential nuclide; calculating the total probability value and the probability standard threshold of each potential nuclide in the radiation field; and calculating the existence probability of each potential nuclide. According to the invention, multiple times of background deduction calculation and standardization are performed on the measured energy spectrum data, and a nuclide can be identified and the existence probability of the nuclide can be calculated quickly based on the total probability value calculated based on the probability statistics principle and the standard threshold calculated by a standard source.

Description

technical field [0001] The invention relates to a method for identifying gamma nuclides, in particular to a method for rapidly identifying gamma nuclides in complex radiation fields. Background technique [0002] In nuclear power plant spent fuel or radioactive waste repositories and processing rooms, there are gamma radioactive nuclides of unknown type and intensity. In order to grasp the potential nuclide information in these environments, it is necessary to use The energy spectrum measurement of the marked gamma rays emitted by the source is carried out, and the measured energy spectrum is analyzed by the nuclide identification method to realize the qualitative judgment and quantitative analysis of the nuclides existing in the radiation field. [0003] The key point in the realization of the nuclide identification method is the effective analysis of the energy spectrum, and when measuring the gamma radioactivity in a complex radiation site, the background (including natur...

AI Technical Summary

Problems solved by technology

In the stripping method, due to the iterative stripping of a single nuclide, the statistical errors are accumulated in the iterations, making it difficult to accurately analyze the isotopic composition with low-energy γ characteristic peaks

The artificial neural network models used for nuclide identification include Bayesian network, optimal linear associative memory (OLAM) neural network, and BP artificial neural network. The shortcomings of the complex γ-ray spectrum with heavy peaks, but they all need a batch of samples with known γ-nuclide composition as learning samples to train the cascade neural network, and then use the trained network to identify all samples in real time , has instability, but for nuclide analysis in complex radiation fields with high stability requirements, it cannot meet the requirements

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