Assessment of Rn-222 continuous time series for the identification of anomalous changes during moderate earthquakes of the Garhwal Himalaya

Shukla, Vaishali; Chauhan, Vishal Singh; Kumar, Naresh; Hazarika, Devajit

doi:10.1016/j.apradiso.2020.109327

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…The two nearby and strongest events suggest pre-seismic variations that are related to earthquake precursor signatures. The observed results are explained in light of the dilatancy-diffusion model [89]. In India, soil radon has been monitored continuously in the Kutch region of Gujarat India to study pre-seismic anomalies.…”

Section: Radon and Earthquakesmentioning

confidence: 80%

Relationships among Indoor Radon, Earthquake Magnitude Data and Lung Cancer Risks in a Residential Building of an Apulian Town (Southern Italy)

et al. 2021

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(1) Background: The association of radon-222 with lung cancer is well studied. The aim of the study was to validate a model of indoor radon measurements, to apply radon software to estimate lung cancer cases that are attributable to radon and to study the relationship between radon and earthquakes. (2) Methods: Different data detectors were used to obtain radon measurements in different places. Continuous data collection and predictions of indoor radon concentrations were carried out. Software was used to assess radon-attributable lung cancer cases, and data related to earthquake magnitudes were downloaded from Italian Vulcanology Institute. (3) Results: As expected, the highest radon concentrations were observed on the ground floor (232 ± 232 Bq/m3), with higher values measured during winter than in other seasons. The comparison of the detectors showed the overlapping of the two detectors-measured data sets. The cases of lung cancer that were attributable to radon in Locorotondo were studied (3.66/10,000). From the multivariate analysis of the relationship between high radon concentrations and high earthquake magnitude values, they show statistically significant ORs of just over 1. (4) Conclusions: Although the measured values are, on average, within the reference level, prevention measures must be implemented, as the measured radon values allow us to estimate an expected value of 3.66 cases of lung cancer per 10,000 people in the resident population.

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Section: Radon and Earthquakesmentioning

confidence: 80%

Relationships among Indoor Radon, Earthquake Magnitude Data and Lung Cancer Risks in a Residential Building of an Apulian Town (Southern Italy)

et al. 2021

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“…However, this remote site in the Higher-Himalaya, away from human generated noise, indicates that there are different background natural noises to make the data very complex. There is a very high hydrological effect in the gravity and radon emanation (Shukla et al, 2020;Chauhan et al, 2021). After removing the background noises, anomalous changes are reported in case of 19-20 moderate magnitude local earthquakes.…”

Section: Earthquake Precursory Studiesmentioning

confidence: 99%

INSA National Report on Seismological Research in India: 2019 – 2022

Manglik

2023

Preprint

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The Indian National Science Academy (INSA), the National Adhering body representing India in the International Union of Geodesy and Geophysics (IUGG), submits a National Report to the IUGG during its General Assembly, held every four years. The present chapter is a compilation for the International Association of Seismology and Physics of the Earth's Interior (IASPEI), one of the eight Associations of the IUGG, encompassing the scientific activities carried out in the country during 2019-2022, in the field of seismology. The chapter is arranged to categorize the contributions under internal structure of the Indian lithosphere and sub-lithospheric mantle, crust and upper mantle deformation, seismic attenuation, seismic hazards, triggered seismicity, environmental seismology, paleoseismology and seismological networks. In addition, the chapter includes activities related to societal projects and outreach programs. It is not an exhaustive review of the entire Indian contribution to seismology and allied fields during this period but a window to the topics covered within the framework of IASPEI.During the reporting period, researchers from several Indian R&D and Academic Institutions continued adding new results on the crustal and upper mantle structure of the Indian shield, both at regional and geological province scale. The Himalaya has been a major focus of seismological research for many Institutes who operated broadband seismological networks from Kashmir to Arunachal along the Himalayan arc. The data from these networks have facilitated in enhancing our understanding of the structure of the Main Himalayan Thrust, crustal and upper mantle structure as well as the dip of the underthrusting Indian plate, seismicity monitoring, and estimation of attenuation. Some of these networks also include accelerographs for strong ground motion studies. A few studies also focused on mapping of the mantle transition zone beneath the Indian Ocean Geoid Low. The studies on anthropogenic seismicity added near-field investigations in the Koyna -Warna region through borehole seismology. Organization of the Joint Scientific Assembly of IAGA and IASPEI during 21-27 August 2021 (JSA-2021) was a major event during this period related to the IUGG activities. Originally, the event was planned to be hosted at Hyderabad, India, but due to the COVID-19 pandemic it was organized in virtual mode. Initiatives have been taken to nucleate the field of environmental seismology in the country.

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“…The abnormal changes in Animal Behavior [3], Temperature [4], [5], Lunglin-1976 and Przhevalsk -Russia 1970, Water Levels [6], [7], Earthquake in 2016 at Kumamoto), Velocity of P-wave (V P ) and Velocity of S-wave (V S ), use as instances for early detection of disasters. ''Damage'' can be defined as the changes in the properties of the building structures, boundary environments and system connectivity such as geo-metric and/or material.…”

Section: Introductionmentioning

confidence: 99%

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

Chaparala²,

Jain

et al. 2023

IEEE Access

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Standard inspections of buildings are not always possible because of human flaws in prediction. Hence, we need more stable, scalable, and efficient automated processes. Structure Health Monitoring (SHM) is one of the automation systems for forecasting potential losses in building structures. This article suggested how to monitor the strength status of buildings by using Hybrid Machine Learning Technique (HMLT). HMLT contains two-hybrid procedures. One for identifying the most significant features in Dataset using Hybrid Feature Selection Method (HFSM). HFSM uses the combined features of Mutual information (MI) and Rough Set Theory (RST) for feature selection. Another method is optimized classifiers such as Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are used for the classification and predicting the accuracy i.e. predicting the strength status of buildings. Now the proposed method is applied on Earthquake Damage Dataset (Gorkha Earthquake in April 2015). Training and 10-fold crossvalidation procedure pragmatic to features. Then the performance of proposed method has been evaluated using the F1-score and accuracy metrics and get 91% and 92% respectively. Finally, the result analysis demonstrates the importance of the proposed approach in predicting the status of the building strength.

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Assessment of Rn-222 continuous time series for the identification of anomalous changes during moderate earthquakes of the Garhwal Himalaya

Cited by 16 publications

References 38 publications

Relationships among Indoor Radon, Earthquake Magnitude Data and Lung Cancer Risks in a Residential Building of an Apulian Town (Southern Italy)

Relationships among Indoor Radon, Earthquake Magnitude Data and Lung Cancer Risks in a Residential Building of an Apulian Town (Southern Italy)

INSA National Report on Seismological Research in India: 2019 – 2022

Monitor the Strength Status of Buildings Using Hybrid Machine Learning Technique

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