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The monitoring of Cs-137 in seawater using scintillation detector relies onthe spectrum analysis method to extract the Cs-137 concentration. And when inpoor statistic situation, the calculation result of the traditional net peakarea (NPA) method has a large uncertainty. We present a machine learning basedmethod to better analyze the gamma-ray spectrum with low Cs-137 concentration.We apply multilayer perceptron (MLP) to analyze the 662 keV full energy peak ofCs-137 in the seawater spectrum. And the MLP can be trained with a few measuredbackground spectrums by combining the simulated Cs-137 signal with measuredbackground spectrums. Thus, it can save the time of preparing and measuring thestandard samples for generating the training dataset. To validate the MLP-basedmethod, we use Geant4 and background gamma-ray spectrums measured by a seabornemonitoring device to generate an independent test dataset to test the result byour method and the traditional NPA method. We find that the MLP-based methodachieves a root mean squared error of 0.159, 2.3 times lower than that of thetraditional net peak area method, indicating the MLP-based method improves theprecision of Cs-137 concentration calculation

A marine radioisotope gamma-ray spectrum analysis method based on Monte Carlo simulation and MLP neural network