Abnormal increases in radon (222Rn, half-life = 3.82 days) activity have occasionally been observed in underground environments before major earthquakes. However, 222Rn alone could not be used to forecast earthquakes since it can also be increased due to diffusive inputs over its lifetime. Here, we show that a very short-lived isotope, thoron (220Rn, half-life = 55.6 s; mean life = 80 s), in a cave can record earthquake signals without interference from other environmental effects. We monitored 220Rn together with 222Rn in air of a limestone-cave in Korea for one year. Unusually large 220Rn peaks were observed only in February 2011, preceding the 2011 M9.0 Tohoku-Oki Earthquake, Japan, while large 222Rn peaks were observed in both February 2011 and the summer. Based on our analyses, we suggest that the anomalous peaks of 222Rn and 220Rn activities observed in February were precursory signals related to the Tohoku-Oki Earthquake. Thus, the 220Rn-222Rn combined isotope pair method can present new opportunities for earthquake forecasting if the technique is extensively employed in earthquake monitoring networks around the world.

/pdf/a-radon-thoron-isotope-pair-as-a-reliable-earthquake-4b9n1g0lt2.pdf

A radon-thoron isotope pair as a reliable earthquake precursor.

The release of radon in active fault zones is a sustained radioactive pollution source of the atmospheric environment. The species, concentration and flux of radon emitted in soil gas in active fault zones in the Capital of China were investigated by in-situ field measurements. Two main species of radon discharging from soil gas in active fault zones were identified, including radon diffused and dispersed from permeable soil, and upwelling from faults. Higher concentrations and flux of radon from faults were observed in the Bohai Bay Basin due to the accumulated uranium in the sandstone reservoirs and higher permeability of the strata and bed rocks. Increased radon released by strong earthquakes persists, with the max flux of 334.56 mBq m−2 s−1 observed in FN (Fengnan district) located at the epicenter of the 28 July, 1976 Tangshan MS 7.8 earthquake. The level of radon released in 8 of 22 locations within the Basin and Range Province (to the west of Taihangshan piedmont fault Zone) reached level 2, and 13 of 14 locations within the Bohai Bay Basin reached levels 2–4, according to the Chinese Code (GB 50325–2001, 2006). Corresponding protective and safety measures should be in place to protect the health of nearby residents, due to their exposure to radon emitted from the faults. Also, the concentration of radon in active fault zones should be investigated to assess the possible risk, before land-use is planned.

/pdf/radon-emission-from-soil-gases-in-the-active-fault-zones-in-13ovcte7gw.pdf

Radon emission from soil gases in the active fault zones in the Capital of China and its environmental effects.

Application of radon (222Rn) as an environmental tracer in hydrogeological and geological investigations: An overview.

Expert systems (ES) are a branch of applied artificial intelligence. The basic idea behind ES is simply that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer. ES provide powerful and flexible means for obtaining solutions to a variety of problems that often cannot be dealt with by other, more traditional and orthodox methods. Thus, their use is proliferating to many sectors of our social and technological life, where their applications are proving to be critical in the process of decision support and problem solving. Earthquake professionals for many decades have recognized the benefits to society from reliable earthquake predictions, but uncertainties regarding source initiation, rupture phenomena, and accuracy of both the timing and magnitude of the earthquake occurrence have often times seemed either very difficult or impossible to overcome. This research proposes and implements an expert system to predict earthquakes from previous data. This is achieved by applying association rule mining on earthquake data from 1972 to 2013. These associations are polished using predicate-logic techniques to draw stimulating production-rules to be used with a rule-based expert system. The proposed expert system was able to predict all earthquakes which actually occurred within 12h at-most.

Developing an expert system based on association rules and predicate logic for earthquake prediction

Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km2), vulnerability (480.98 km2), and risk (34,586.10 km2) was estimated.

Earthquake risk assessment in NE India using deep learning and geospatial analysis

This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

Correlation of radon anomalies with seismic events along Mat Fault in Serchhip district, Mizoram, India.

Continuous soil-gas radon and thoron measurements were carried out at 15 days interval along Mat fault in Mizoram (India), which lies in the seismic zone V of the seismic zonation map of India. The study was carried out from March 2012 to February 2013 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. The effect of meteorological parameters on radon and thoron data was taken into consideration. The annual average value of radon and thoron concentration was found to be 621.21 and 590.18 Bq/m3 with a standard deviation of 377.60 and 301.34 Bq/m3, respectively. Inverse correlation coefficient was obtained between radon/thoron concentration and the meteorological parameters except in one case (thoron and relative humidity) which showed a weak positive correlation. Standard deviation method was employed in order to differentiate those anomalies which are solely caused by seismic events and not by meteorological parameters. The data obtained have been correlated with the seismic activities that occurred around the measuring site. Positive correlations were found between radon/thoron data and the earthquakes.

Temporal variation of soil radon and thoron concentrations in Mizoram (India), associated with earthquakes

In this study, radon and thoron concentrations in soil gas has been monitored using LR-115(II) solid state nuclear track detectors since 15th July 2011 to February 2012. The study was carried out along Mat fault in Serchip district, Mizoram, India at two different sites – Mat Bridge (23°18′N, 92°48′E) and Tuichang (23°13′N, 92°56′E). The results obtained have been correlated to the seismic events that occurred within 800 km from the measuring sites over the mentioned period of time. Anomalous behaviour in radon concentrations have been observed prior to some earthquakes. Interestingly, some thoron anomalies were also recorded.

/pdf/radon-and-thoron-anomalies-along-mat-fault-in-mizoram-india-3oq6q4m6q3.pdf

Radon and thoron anomalies along Mat fault in Mizoram, India

Maximum entropy condition in queueing theory of Silviu Guiasu has been subjected to further study in the present investigation. We have derived interarrival time by maximizing the entropy condition using gamma distribution. The paper ends with interesting and useful results in the form of two theorems, where the probability distribution and maximum entropy condition of the possible states of the system for multiserver queueing system has been derived.

Maximum entropy condition in multiserver queueing system

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