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Enhanced radon concentration denoising in cemented coal gangue backfill: A novel hybrid VMD-SVD-SWT framework
Zekun Feng1, Dongting Yang1, Yong Chen1
1School of Engineering and Technology, China University of Geosciences (Beijing), Beijing, 100083, China.
Abstract:
Due to radioactive interference, there are many challenges in monitoring the concentration of radon in filling materials. Accurately determining the mode of radon release is crucial to assessing environmental impacts, ensuring public health and promoting green mining practices. This research integrates XSine chaotic mapping, spiral search and Sparrow-Krill mechanism to improve the sand cat group optimization algorithm, and proposes a VMD-SVD-SWT radon concentration data denoising method based on the improvement of SSSK-SCSO algorithm optimization. This denoising method can decompose signals, enhance principal components and purify multi-scale details in radon data. The test of the actual radon concentration monitoring data shows that the method is superior to the traditional method in terms of smoothness, correlation coefficient and E-Kurtosis, among which the first two indicators are improved by 10.6% and 72.5% respectively. This shows that the method can effectively inhibit the interference of radioactive decay while preserving the characteristics and trends of radon release. It provides key technical support for the accurate management of the radiation environment and ecological risk assessment of mines, and provides clear engineering application value for promoting the control of radioactive pollution sources and ensuring health and safety.
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