A sensitive system based on radon amplification at soil-air interface: Aiming to advance earthquake precursor research

Radon, a natural radioactive gas, serves as a valuable tracer in geophysical research and atmospheric science such as detecting stress induced signal in bedrock. However, the conventional radon monitoring methods often lack the sensitivity required to accurately capture such signals. This limitation...

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Bibliographic Details
Published in:Journal of environmental radioactivity 2024-09, Vol.278, p.107482, Article 107482
Main Authors: Sahoo, B.K., Sapra, B.K., Kanse, S.D., Gaware, J.J., Kumbhar, D.H., Ratheesh, M.P., Khan, Arshad, Aswal, D.K.
Format: Article
Language:English
Subjects:
BhaROSA
Radon
Remote sensing
Signal amplification
Tracer
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Summary:Radon, a natural radioactive gas, serves as a valuable tracer in geophysical research and atmospheric science such as detecting stress induced signal in bedrock. However, the conventional radon monitoring methods often lack the sensitivity required to accurately capture such signals. This limitation, coupled with interference from meteorological effects, poses challenges in distinguishing genuine stress-induced signals. In this study, we propose a novel approach utilizing radon concentration gradients at the soil-air interface to enhance sensitivity and detect stress induced radon signals more effectively. Drawing from pressure diffusion models, we demonstrate how seismic stress accumulation in bedrock alters radon profiles in the sub-soil, providing insights into the mechanisms underlying stress-induced radon variations. Building upon this theoretical framework, we introduce the “Bhabha Radon Observatory for Seismic Application (BhaROSA)," a remote sensing, solar-powered radon observatory designed for widespread deployment and continuous unattended monitoring for big database generation. Field experiments comparing BhaROSA's performance to conventional soil probe techniques validate and confirm the superior sensitivity in line with theoretical predictions. This innovative approach holds promise for improving our understanding of stress dynamics in bedrock and has potential applications in various geophysical and atmospheric science such as earthquake precursory research, geo-genic radon potential and risk assessment. To progress, we propose international alliance and application of deep learning to a big database of precursor signals, which may lead to more informed conclusions on earthquake predictability-an enduring and unsolved challenge for humanity. [Display omitted] •Modelling insights into Radon transport for tracer application.•Novel gradient-based approach enhances sensitivity to stress induced radon signal.•Field experiments validate superior sensitivity.•Introducing BhaROSA: A Remote Sensing Solar-powered Radon Observatory.•Bigdata generation for Geophysical and Atmospheric Science.
ISSN:0265-931X
1879-1700
1879-1700
DOI:10.1016/j.jenvrad.2024.107482