Development of Convolutional Neural Networks to Estimate Depth Distribution of Radioisotope in Soil Layers

Abstract

To devise decontamination plans for soil, we have been developing a portable monitoring system that estimates the distribution of radioactive soil contaminants in the depth direction. The system consists of radiation sensors and an estimator using a convolutional neural network (CNN). The rod-shaped measuring instrument, which can be embedded in the soil, has 20 radiation sensors arranged at intervals of 2.5 cm. In our previous study, to create a CNN that can estimate the depth distribution of radioisotopes (RIs) when RIs are only in one layer, we made a large number of simulation datasets to train and validate the CNN. The trained CNN was able to estimate the depth distribution of RIs from the simulation data. However, the CNN should be improved so that it works well for practical situations such as when RIs exist in more than one layer. In this paper, we present an improvement of the CNN so that it can work for RIs in three layers at maximum.