Radioisotope Identification and Nonintrusive Depth Estimation of Localized Low-Level Radioactive Contaminants Using Bayesian Inference

Radioisotope Identification and Nonintrusive Depth Estimation of Localized Low-Level Radioactive Contaminants Using Bayesian Inference

Obtaining the in-depth information of radioactive contaminants is crucial for determining the most cost-effective decommissioning strategy. The main limitations of a burial depth analysis lie in the assumptions that foreknowledge of buried radioisotopes present at the site is always available and th...

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Bibliographic Details
Main Authors: Jinhwan Kim, Kyung Taek Lim, Kilyoung Ko, Eunbie Ko, Gyuseong Cho
Format: Article
Language:English
Published: MDPI AG 2019-12-01
Series:Sensors
Subjects:
remote depth profiling
radioisotope identification
bayesian inference
uncertainty estimation
gamma spectral analysis
low-level radioactive contaminants
nuclear decommissioning
low-resolution detector
Online Access:https://www.mdpi.com/1424-8220/20/1/95

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