Simplex optimization of artificial neural networks for the prediction of minimum detectable activity in gamma-ray spectrometry

2006 ◽  
Vol 564 (1) ◽  
pp. 308-314 ◽  
Author(s):  
Snežana Dragović ◽  
Antonije Onjia ◽  
Goran Bačić
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gamma Ray ◽  
Minimum Detectable Activity ◽  
Gamma Ray Spectrometry ◽  
Simplex Optimization ◽  
Detectable Activity ◽  
Artificial Neural

scholarly journals Study of the minimum detectable activity in gamma-ray spectrometry with various shielding configurations

2017 ◽  
Vol 1 (T4) ◽  
pp. 56-62
Author(s):  
Vuong Quang Le ◽  
Nguyen Hoang Vo ◽  
Chuong Dinh Huynh ◽  
Phuc Minh Lau ◽  
Thanh Thien Tran ◽  
...  
Keyword(s):  
Environmental Samples ◽  
Gamma Ray ◽  
Background Radiation ◽  
Environmental Radioactivity ◽  
Minimum Detectable Activity ◽  
The Other ◽  
Gamma Ray Spectrometry ◽  
Gamma Spectrometer ◽  
Detectable Activity ◽  
Gamma Ray Spectrometer

In the environmental radioactivity analyzing methods using gamma-ray spectrometry, the natural activities of radionuclides were required to be higher than the minimum detectable activity (MDA). To reduce MDA, one of the popular methods is to improve the ability of reducing the background radiation of the gamma-ray spectrometry. In this work, we designed the shielding configuration with 5 cm lead and 2 mm copper (thickness of walls and top). The MDAs of gamma-ray spectrometer were 2.6–4.24 times times for 40K (1460.8 keV), 232Th (208Tl- 2614.5 keV) and 238U (214Pb- 352 keV; 214Bi- 609.3 keV, 214Bi- 1764.5 keV). In the other hand, MDA for 238U with this shielding configuration is smaller than the activity of 238U inside surface soils in Vietnam. These results showed that the gamma spectrometer with NaI(Tl) detector and this shielding configuration was suitable for measurements activity of 238U in the environmental samples.

Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale from the Spectral Gamma-Ray and Based on the Artificial Neural Networks

2021 ◽  
pp. 1-28
Author(s):  
Ahmed Abdulhamid Mahmoud ◽  
Salaheldin Elkatatny
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Linear Regression Analysis ◽  
Empirical Correlation ◽  
Training Data ◽  
Validation Data ◽  
Artificial Neural

Abstract Evaluation of the quality of unconventional hydrocarbon resources becomes a critical stage toward characterizing these resources, this evaluation requires evaluation of the total organic carbon (TOC). Generally, TOC is determined from laboratory experiments, however, it is hard to obtain a continuous profile for the TOC along the drilled formations using these experiments. Another way to evaluate the TOC is through the use of empirical correlation, the currently available correlations lack the accuracy especially when used in formations other than the ones used to develop these correlations. This study introduces an empirical equation for evaluation of the TOC in Devonian Duvernay shale from only gamma-ray and spectral gamma-ray logs of uranium, thorium, and potassium as well as a newly developed term that accounts for the TOC from the linear regression analysis. This new correlation was developed based on the artificial neural networks (ANN) algorithm which was learned on 750 datasets from Well-A. The developed correlation was tested and validated on 226 and 73 datasets from Well-B and Well-C, respectively. The results of this study indicated that for the training data, the TOC was predicted by the ANN with an AAPE of only 8.5%. Using the developed equation, the TOC was predicted with an AAPE of only 11.5% for the testing data. For the validation data, the developed equation overperformed the previous models in estimating the TOC with an AAPE of only 11.9%.

scholarly journals Automatic Design of Artificial Neural Networks for Gamma-Ray Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110531-110540 ◽  
Author(s):  
Filipe Assuncao ◽  
Joao Correia ◽  
Ruben Conceicao ◽  
Mario Joao Martins Pimenta ◽  
Bernardo Tome ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gamma Ray ◽  
Automatic Design ◽  
Artificial Neural
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