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Identification of pre-seismic radon anomaly using artificial neural network near Indo-Burman subduction line

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Abstract

The study presents analysis of 1 year 15 min cycle radon data using artificial neural network (ANN) in an attempt to estimate earthquake prediction time near Indo-Burman subduction line. The region was found to be seismically active and radon anomalies respond well to it, when majority of the radon anomalies peaks and selected earthquakes correlated. The observation also shows that application of non-linear technique ANN to a non-linear data like radon seems a promising approach in predicting geophysical phenomena at one of the most seismically active region of the world.

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Acknowledgements

This work was supported financially by DAEBRNS, BARC, Mumbai, India [Sanction Order No.:36(4)/14/66/2014-BRNS/36024 Dt.26.02.2016.]

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  1. Department of Physics, Mizoram University, Aizawl, Mizoram, 796004, India

    T. Thuamthansanga & Ramesh Chandra Tiwari

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T.T.T.S (PhD student) carried out the fieldwork, generated data, performed data analysis, wrote and revised the manuscript. R.C.T (Professor) guide the research work and acquired the research grant.

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Correspondence to T. Thuamthansanga.

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Thuamthansanga, T., Tiwari, R.C. Identification of pre-seismic radon anomaly using artificial neural network near Indo-Burman subduction line. J Radioanal Nucl Chem (2024). https://doi.org/10.1007/s10967-024-09487-6

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