Some Experimental Sampling Results for Regression Analysis Applied to Gamma Ray Spectrometer Data, II

Biometrics ◽  
1968 ◽  
Vol 24 (2) ◽  
pp. 353 ◽  
Author(s):  
Bernard S. Pasternack ◽  
Anthony Liuzzi
Keyword(s):  
Regression Analysis ◽  
Gamma Ray ◽  
Gamma Ray Spectrometer ◽  
Spectrometer Data ◽  
Experimental Sampling

Gamma-ray spectrometer calibration for field analysis of thorium, uranium and potassium

1970 ◽  
Vol 7 (4) ◽  
pp. 1093-1098 ◽  
Author(s):  
P. G. Killeen ◽  
C. M. Carmichael
Keyword(s):  
Regression Analysis ◽  
Gamma Ray ◽  
Laboratory Analysis ◽  
In Situ Analysis ◽  
Field Analysis ◽  
Gamma Ray Spectrometer ◽  
Nonlinear Relation ◽  
Using Data ◽  
Spectrometer Calibration

The calibration of a portable three-channel gamma-ray spectrometer for in situ analysis of thorium, uranium, and potassium is discussed. A method of regression analysis is suggested as the best means of including all of the data available from the calibration stations. Calibration indicates a nonlinear relation between count rates obtained in the field and concentrations in parts per million obtained from laboratory analysis. The range of radioelement content must be taken into consideration and appropriate sets of calibration constants applied. As an example of the method, calibration constants are calculated for a portable gamma-ray spectrometer using data for the Blind River uranium region of Ontario.

Background deduction of the Chang’E-1 gamma-ray spectrometer data

2012 ◽  
Vol 31 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Liyan Zhang ◽  
Chunlai Li ◽  
Jinazhong Liu ◽  
Yongliao Zou ◽  
Ziyuan Ouyang
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Spectrometer ◽  
Spectrometer Data

Gamma-Ray Spectrum Analysis of Chang’E-1 for Lunar Detection

2010 ◽  
Vol 133 (5) ◽  
Author(s):  
Xu HongKun ◽  
Fang Fang ◽  
Ni Shijun ◽  
He Jianfeng ◽  
You Lei
Keyword(s):  
Spectral Characteristic ◽  
Spectrum Analysis ◽  
Gamma Ray ◽  
Low Energy ◽  
Elemental Abundance ◽  
Nuclear Engineering ◽  
Gamma Ray Spectrometer ◽  
Lunar Prospector ◽  
Spectrometer Data ◽  
Wavelet Methods

Gamma-ray spectrum analysis was essential for radioactive environmental monitoring, and it had been widely used in many areas of nuclear engineering. However, for the low-energy region of gamma-ray spectrum, weak peaks were contained in the fast-decreasing background, so it was difficult to extract characteristic information from original spectra. In order to get a better analytic result based on wavelet methods in frequency domain, we had processed the gamma-ray spectrometer data of Chang’E-1 and well extracted some useful information of spectral characteristic peaks. Then, we preliminarily mapped the distribution of net peak counts for potassium on lunar surface, which indirectly reflected the distribution of elemental abundance. At last, we compared our analytic result with that of Apollo and Lunar Prospector and found some consistencies and differences.

The collection and processing of airborne gamma ray spectrometer data

1974 ◽  
Vol 12 (2-3) ◽  
pp. 218
Author(s):  
P.J. McSharry ◽  
D.W. Emerson
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Spectrometer ◽  
Spectrometer Data

Statistical analysis of airborne gamma‐ray data for geologic mapping purposes: Crixas‐Itapaci area, Goias, Brazil

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1326-1332 ◽  
Author(s):  
A. C. B. Pires ◽  
N. Harthill
Keyword(s):  
Statistical Analysis ◽  
Gamma Ray ◽  
Mineral Exploration ◽  
Aerial Survey ◽  
Geologic Mapping ◽  
Good Correspondence ◽  
Gamma Ray Spectrometer ◽  
Data Points ◽  
Spectrometer Data ◽  
Mode Technique

Q‐mode factor analysis, K‐means clustering, and G‐mode clustering were used on digitized gamma‐ray spectrometer data from an aerial survey of the Crixas‐Itapaci area, Goias, Brazil. The data points including seven variables—eU, eTh, K, total count, U/Th, U/K, and Th/K—were digitized for a 2 km square grid. For the northwest corner of the area the data were gridded at 1 km. The Q‐mode classification method supplied results that do not show a good correspondence with the known geology. The K‐means clustering procedure barely identified the main lithologic features of the area. The G‐mode technique produced results that correlate well with the known geology and identified the greenstone belts present in the area by discriminating their ultramafic and mafic components from adjacent felsic rocks. Statistical analysis of aerial gamma‐ray spectrometer data can be very helpful in mapping geologic units in poorly known areas. It can also be used for mineral exploration purposes if mineralization is known to be associated with lithologies that can be identified by the techniques used in this study.

Derivation of elemental abundance maps at intermediate resolution from optical interpolation of lunar prospector gamma-ray spectrometer data

2005 ◽  
Vol 53 (12) ◽  
pp. 1287-1301 ◽  
Author(s):  
Yuriy G. Shkuratov ◽  
Vadim G. Kaydash ◽  
Dmitriy G. Stankevich ◽  
Larissa V. Starukhina ◽  
Patrick C. Pinet ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Elemental Abundance ◽  
Gamma Ray Spectrometer ◽  
Lunar Prospector ◽  
Spectrometer Data
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