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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Deep geologic repository progress—2025 Update
Editor's note: This article has was originally published in November 2023. It has been updated with new information as of June 2025.
Outside my office, there is a display case filled with rock samples from all over the world. It contains a disk of translucent, orange salt from the Waste Isolation Pilot Plant near Carlsbad, N.M.; a core of white-and-bronze gneiss from the site of the future deep geologic repository in Eurajoki, Finland; several angular chunks of fine-grained, gray claystone from the underground research laboratory at Bure, France; and a piece of coarse-grained granite from the underground research tunnel in Daejeon, South Korea.
Chenhao Zeng, Wanchang Lai, Jinge Zhou, Hongjian Lin, Xiaojie Feng, Yongping Yu, Runqiu Gu, Shangqing Sun, Jinfei Wu
Nuclear Technology | Volume 209 | Number 4 | April 2023 | Pages 549-559
Technical Paper | doi.org/10.1080/00295450.2022.2133515
Articles are hosted by Taylor and Francis Online.
We address the performance of airborne gamma detection systems equipped with a NaI(Tl) detector to monitor radionuclides in specific areas. In particular, we analyze the use of the fast singular value decomposition (FSVD) algorithm to improve the nuclide recognition ability of the system and effectively trace radioactivity in a complex background environment. We first present a theoretical analysis of the FSVD algorithm and illustrate the nuclide recognition algorithm step by step. The core of the algorithm is singular value decomposition and parameter estimation based on a Gaussian Markov linear regression model. From the estimated values of the parameters, information about radionuclides can be effectively extracted. We assume the presence of a strong background due to a high concentration of 222Rn and its progeny, which is simulated using GEANT4. By adding trace elements of 131I and 137Cs and changing the relative emissivity, the ratio of the total energy peak count of 131I and 137Cs to the background environment interval count of the corresponding 222Rn and its progeny are controlled. Assuming a counting ratio equal to 0.005, the FSVD algorithm is still able to effectively discriminate the presence of a small number of nuclides, reflecting very excellent recognition ability. Finally, based on data from an airborne gamma detection system in a self-control radon chamber, the FSVD algorithm is employed to recognize the trace of 137Cs nuclides in a strong radon background. A DURRIDGE RAD7 radon measuring instrument is used to monitor the radon concentration in the radon chamber. The actual measurement results show that the FSVD algorithm can effectively detect 137Cs nuclides.
