Gamma-ray spectrometry (GRS) is an exploration technology that distinguishes itself from other non-contact sensing technologies because it provides information from 30 to 50 cm below the ground. This technology has evolved through three significant turning points in mapping output. The first turning point, in the 1960s-1970s, was the transition from U concentration maps to weathered zoning maps utilizing K or eTh. The second turning point, occurring from the 1980s to 1990s, was marked by the application of radionuclide mapping to assess radioactive contamination. A third turning point, in the early 2000s, was the development of soil maps for precision agriculture, supported by the free statistics software R. This paper reviews advances in gamma-ray spectrometry spectral analysis since 2000. Traditionally, the gamma-ray spectrum is depicted as a two-dimensional graph with energy on the horizontal axis and counts on the vertical axis. The NASVD and MNF methods, developed around 2000, necessitate a reevaluation of this concept. By conducting principal component analysis of the gamma-ray spectrum in hyperspace, these techniques have unveiled new spectra, such as ground and sky spectra, and have facilitated the removal of noise components from the gamma-ray spectrum. Naturally occurring gamma-ray spectra typically exhibit energies ranging from 0.04 to 3 MeV. Observations from fusion reactors measure energies up to 20 MeV for diagnostics of nuclear plasma. These spectra may yield valuable insights when applied to innovative statistical analysis techniques. A comprehensive spectral analysis method developed in the early 2000s has demonstrated the potential to extract a variety of information beyond window nuclides, previously unexplored. The regression coefficient plots from the PLSR regression model have revealed novel spectral images. This model is set to influence future research on GRS by expanding the number of objectives and covariates. The innovative calibration method for full-spectrum analysis, which assesses different concentration areas, has proven that calibration is achievable even in the absence of a calibration pad. It is expected to become a formidable approach for spectrum analysis in the upcoming period.
| Published in | Nuclear Science (Volume 9, Issue 1) |
| DOI | 10.11648/j.ns.20240901.12 |
| Page(s) | 8-29 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Hyperspace, Principal Component Analysis, Regression Analysis, Partial Least Squares Regression Analysis, Regression Coefficients Plots, Full-Spectral Analysis
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APA Style
Imaizumi, M. (2024). Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry. Nuclear Science, 9(1), 8-29. https://doi.org/10.11648/j.ns.20240901.12
ACS Style
Imaizumi, M. Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry. Nucl. Sci. 2024, 9(1), 8-29. doi: 10.11648/j.ns.20240901.12
AMA Style
Imaizumi M. Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry. Nucl Sci. 2024;9(1):8-29. doi: 10.11648/j.ns.20240901.12
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Download@article{10.11648/j.ns.20240901.12,
author = {Masayuki Imaizumi},
title = {Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry
},
journal = {Nuclear Science},
volume = {9},
number = {1},
pages = {8-29},
doi = {10.11648/j.ns.20240901.12},
url = {https://doi.org/10.11648/j.ns.20240901.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ns.20240901.12},
abstract = {Gamma-ray spectrometry (GRS) is an exploration technology that distinguishes itself from other non-contact sensing technologies because it provides information from 30 to 50 cm below the ground. This technology has evolved through three significant turning points in mapping output. The first turning point, in the 1960s-1970s, was the transition from U concentration maps to weathered zoning maps utilizing K or eTh. The second turning point, occurring from the 1980s to 1990s, was marked by the application of radionuclide mapping to assess radioactive contamination. A third turning point, in the early 2000s, was the development of soil maps for precision agriculture, supported by the free statistics software R. This paper reviews advances in gamma-ray spectrometry spectral analysis since 2000. Traditionally, the gamma-ray spectrum is depicted as a two-dimensional graph with energy on the horizontal axis and counts on the vertical axis. The NASVD and MNF methods, developed around 2000, necessitate a reevaluation of this concept. By conducting principal component analysis of the gamma-ray spectrum in hyperspace, these techniques have unveiled new spectra, such as ground and sky spectra, and have facilitated the removal of noise components from the gamma-ray spectrum. Naturally occurring gamma-ray spectra typically exhibit energies ranging from 0.04 to 3 MeV. Observations from fusion reactors measure energies up to 20 MeV for diagnostics of nuclear plasma. These spectra may yield valuable insights when applied to innovative statistical analysis techniques. A comprehensive spectral analysis method developed in the early 2000s has demonstrated the potential to extract a variety of information beyond window nuclides, previously unexplored. The regression coefficient plots from the PLSR regression model have revealed novel spectral images. This model is set to influence future research on GRS by expanding the number of objectives and covariates. The innovative calibration method for full-spectrum analysis, which assesses different concentration areas, has proven that calibration is achievable even in the absence of a calibration pad. It is expected to become a formidable approach for spectrum analysis in the upcoming period.
},
year = {2024}
}
TY - JOUR T1 - Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry AU - Masayuki Imaizumi Y1 - 2024/05/10 PY - 2024 N1 - https://doi.org/10.11648/j.ns.20240901.12 DO - 10.11648/j.ns.20240901.12 T2 - Nuclear Science JF - Nuclear Science JO - Nuclear Science SP - 8 EP - 29 PB - Science Publishing Group SN - 2640-4346 UR - https://doi.org/10.11648/j.ns.20240901.12 AB - Gamma-ray spectrometry (GRS) is an exploration technology that distinguishes itself from other non-contact sensing technologies because it provides information from 30 to 50 cm below the ground. This technology has evolved through three significant turning points in mapping output. The first turning point, in the 1960s-1970s, was the transition from U concentration maps to weathered zoning maps utilizing K or eTh. The second turning point, occurring from the 1980s to 1990s, was marked by the application of radionuclide mapping to assess radioactive contamination. A third turning point, in the early 2000s, was the development of soil maps for precision agriculture, supported by the free statistics software R. This paper reviews advances in gamma-ray spectrometry spectral analysis since 2000. Traditionally, the gamma-ray spectrum is depicted as a two-dimensional graph with energy on the horizontal axis and counts on the vertical axis. The NASVD and MNF methods, developed around 2000, necessitate a reevaluation of this concept. By conducting principal component analysis of the gamma-ray spectrum in hyperspace, these techniques have unveiled new spectra, such as ground and sky spectra, and have facilitated the removal of noise components from the gamma-ray spectrum. Naturally occurring gamma-ray spectra typically exhibit energies ranging from 0.04 to 3 MeV. Observations from fusion reactors measure energies up to 20 MeV for diagnostics of nuclear plasma. These spectra may yield valuable insights when applied to innovative statistical analysis techniques. A comprehensive spectral analysis method developed in the early 2000s has demonstrated the potential to extract a variety of information beyond window nuclides, previously unexplored. The regression coefficient plots from the PLSR regression model have revealed novel spectral images. This model is set to influence future research on GRS by expanding the number of objectives and covariates. The innovative calibration method for full-spectrum analysis, which assesses different concentration areas, has proven that calibration is achievable even in the absence of a calibration pad. It is expected to become a formidable approach for spectrum analysis in the upcoming period. VL - 9 IS - 1 ER -
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