Tareen Aleem Dad Khan, Asim Khawaja M, Kearfott Kimberlee Jane, Rafique Muhammad, Nadeem Malik Sajjad Ahmed, Iqbal Talat, Rahman Saeed Ur
Department of Physics University of Azad Jammu and Kashmir Muzaffarabad, 13100, Azad Kashmir, Pakistan.
Centre for Earthquake Studies National Centre for Physics, Quaid e Azam University, Islamabad, Pakistan.
J Environ Radioact. 2019 Jul;203:48-54. doi: 10.1016/j.jenvrad.2019.03.003. Epub 2019 Mar 9.
In this article, three computational intelligence (CI) models were developed to automatically detect anomalous behaviour in soil radon gas (Rn) time series data. Data were obtained at a fault line and analysed using three machine learning techniques with the aim at identifying anomalies in temporal radon data prompted by seismic events. Radon concentrations were modelled with corresponding meteorological and statistical parameters. This leads to the estimation of soil radon gas without and with meteorological parameters. The comparison between computed radon concentration and actual radon concentrations was used in finding radon anomaly based upon automated system. The anomaly in radon time series data could be considered due to noise or seismic activity. Findings of study show that under meticulously characterized environments, on exclusion of noise contribution, seismic activity is responsible for anomalous behaviour seen in radon time series data.
在本文中，开发了三种计算智能（CI）模型，用于自动检测土壤氡气（Rn）时间序列数据中的异常行为。数据是在一条断层线上获取的，并使用三种机器学习技术进行分析，目的是识别由地震事件引发的氡气时间数据中的异常情况。用相应的气象和统计参数对氡浓度进行建模。这导致了在不考虑和考虑气象参数的情况下对土壤氡气的估计。基于自动化系统，通过比较计算出的氡浓度和实际氡浓度来发现氡异常。氡时间序列数据中的异常可能是由于噪声或地震活动引起的。研究结果表明，在精心表征的环境中，排除噪声影响后，地震活动是氡时间序列数据中异常行为的原因。
