How can radon gas be used by seismologists

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Beijing: Science Press Kang, J. Beijing: Geology Press Yang, T. Exhalation of radon and its carrier gases in SW Taiwan. Italiano, F. Chemical Geology 2 , — Wang, X. This devastating earthquake was also responsible for the triggering of more than 15, geohazards landslides, rockfall, and debris flow which resulted in about 20, casualties [ 39 ].

The destruction caused by the event highlights the importance of earthquake precursory studies in seismically active regions to minimize the loss of life and property. The methodology adopted in the current study is divided mainly into two stages: a instrumental setup and b theoretical setup.

The instrumental setup explains the strategy adopted for the data collection and a brief description of the instrument used for radon monitoring, while the theoretical setup explains about the statistical analysis of radon data for identification of radon anomalies possibly induced by earthquake activity. Since , immediately following the Xingtai earthquake sequence March 22, , China has started an organized, persistent, and systematic effort to testify the postulate of preearthquake anomalies in the context of earthquake forecasting by the China Earthquake Administration CEA.

This research programme was primarily focused on earthquake prediction and its implications to minimize the irrevocable destruction posed by major earthquakes. The CEA has mainly classified this research programme into the following classifications: imminent weeks to days, even hours , short months to weeks , medium years , and long-term decades earthquake forecasting.

In particular, the CEA is operating a very dense radon monitoring network for the surveillance of earthquake activity. For earthquake forecasting research, Rn is more preferred due to its comparatively longer half-life and easy detectability [ 14 ]. In the current study, we have analysed the variations of continuous groundwater Rn concentration associated with the devastating Wenchuan earthquake May 12, ; Mw 7. The details of the Rn monitoring stations regarding its location and distance from the event epicentre are presented in Table 1.

The Rn data used in the current research was acquired by means of two instruments. For automatic continuous sampling, the mixture of escaped gas and water coming from well was passed through a degasser and gas-collecting device and then collected into a ZnS Ag detector system for Rn concentration measurement. This is a digitized method of measurement, and the observation equipment is mainly SD-3A with sampling interval of one hour and a measurement precision of 0.

For the acquisition of the daily variation in Rn concentration, water from the well was sampled and degassed by bubbling degassing and then transported into an ion chamber or ZnS Ag detector, where the Rn concentration was measured by an ionization or scintillation method using FD instrument. The measurement precision is also 0. The further detail of instrumental setups used for Rn monitoring is provided by [ 51 ].

Precipitation is not monitored by the CEA for all the stations used in the current research, and therefore, such data is not available. To overcome this issue, the precipitation dataset of the Tropical Rainfall Measuring Mission TRMM satellite for the year was utilized in this paper.

The TRMM dataset was considered as the best solution among the available sources of precipitation data for China. The negative correlation observed between groundwater Rn anomalies and rainfall for the whole year and the selected time frame is presented in Table 2. The dynamics of the Rn time series show a very complex nonlinear temporal pattern normally characterized by nonstationary and multiscale features [ 52 ].

This chaotic regime of the time series is realized through diurnal, seasonal, multiyear, and decadal Rn cycles along with key influencing parameters [ 14 , 53 , 54 ]. Therefore, the Rn time series is subjected to fractal estimates to determine the degree of chaotic behaviour of Rn and intrinsic long-memory correlations, if any [ 55 ]. Besides this, the estimation of fractal elements for the Rn time series leads to further exploration of the underlying dynamics of physical systems such as seismic activity [ 56 ].

In this regard, the fractal quantity known as the Hurst exponent is calculated for the Rn time series using the rescale-range analysis. The estimation of is based on the following relationships: where is the range, is the standard deviation, is the Hurst exponent, and is the number of entries in a group.

Here, the set of observations is divided into nonoverlapping intervals of , , whereas with individual. Afterwards, the Rn time series is categorized as antipersistent , random , and persistent based on the obtained value of. In particular, the antipersistency means that low present values will probably be followed by high figure values and vice versa. Persistency exhibits that a long-lasting autocorrelation exists within the time series, which implies that high present values will probably be followed by high future values and vice versa.

And random walk means that they are uncorrelated or do not possess long memory trend [ 14 , 56 ]. The inspection of fractal dynamics of Rn time series allows for the identification of anomalous Rn variations if they exist. For reliable identification of an earthquake anomaly, the monitoring station must lie within the Earthquake Preparation Zone EPZ. The EPZ is defined as an area within which the premonitoring fluctuations associated with the tectonically induced impending earthquake can be observed.

In [ 57 ], an empirical relationship is proposed for EPZ ; km based on the magnitude of the earthquake event, given as where the epicentral distance ; km between the monitoring site and the event epicentre is calculated as where. In an ideal scenario, only those events having are considered for earthquake forecasting studies.

In our case, all the stations are lying within as proposed by [ 57 ] and adopted in numerous studies [ 14 , 18 , 22 ]. In addition to all the above, the acquired groundwater Rn data is investigated for identification of anomalous periods possibly linked with this particular event.

The rationale behind the selection of this flexible time window is to analyse the effect of whole aftershock sequence of the Wenchuan earthquake. The identified anomalous periods were further analysed using residual signal processing techniques to eliminate the regular filtering effect as indicated in numerous studies [ 14 , 22 , 59 ]. The residual Rn is calculated via a relationship given as where is the daily average Rn concentration and is the 7-day rolling average Rn concentration.

The data analysis reveals anomalous fluctuations of Rn at a few stations under normal meteorological conditions, which highlights the aspect of a tectonically induced Rn anomaly, while the inspection of the earthquake catalogue of this selected period owns the aspect of tectonically induced radon anomaly due to the Wenchuan earthquake that occurred on May 12, , in Sichuan, China.

Details of the earthquake are presented in Table 3. In this regard, we performed a detailed statistical analysis of Rn concentrations, in order to verify the possible correlation of the Rn anomalies with this particular event. The Rn monitoring stations included in the current study are presented in Figure 2. The annual variation of Rn recorded at selected monitoring stations is presented in Figure 3. Figure 3 indicates a few periods April-June of abnormal rise and fall of Rn around the time of the Wenchuan earthquake.

For example, the MSS station shows a sudden rise of the Rn level from This rise in the Rn level continues onward throughout the whole year. An analogous change of the Rn level was also observed at the relatively distant monitoring stations with Table 1 ; Figures 3 h and 3 i. Based on the results of the preliminary investigation, the anomalous periods of the Rn concentration are subjected to a detailed analysis.

This detailed analysis includes the advanced residual signal processing techniques which remove the regular filtering effects from data if any. Besides this, a statistical criterion of is also applied to the residual Rn to further authenticate our results. The result of this detailed analysis is presented in two stages: a station is located very near to the event epicentre and b station is located far away from the event epicentre for comparison purposes.

Prior to analysing the Rn data for earthquake forecasting research, we have determined the dynamics of the Rn time series using fractal dimensions such a Hurst exponent. The Hurst exponent reveals that the time series follows a persistent trend for all the Rn monitoring stations with insignificant fluctuations as presented in Figure 4.

A positive autocorrelation is found to exist in the recorded Rn data. This suggests that the past trend of data is continued in the future and there is no existence of an irregular trend.

At the first stage, detail analysis of Rn is performed for the station located near the event epicentre as presented in Figures 5 and 6. It includes 7 monitoring stations located in the proximity of the LMSF zone having. The temporal variation of the residual Rn concentration was observed from Mar 1 to June 30, , along with daily precipitation records. The Rn levels seem to be within normal limits until the occurrence of the Wenchuan earthquake on May 12, On May 12, , the Rn levels breach the threshold of anomaly selection and show a sudden upsurge from 0 to 2.

This particular change in the Rn level followed by the Wenchuan earthquake highlights the aspect of the post earthquake Rn anomaly, while the daily Rn variations recorded at the MXS stations show a gradual increase in Rn from April to June with insignificant precipitation Figures 3 b — 5 b.

A detailed inspection of Rn shows that the residual Rn level passed the anomaly selection criteria prior to the Wenchuan earthquake. This preearthquake Rn anomaly seems to be absent after event occurrence Figure 5 b. The KDS monitoring station shows unambiguous changes in the residual Rn levels around the time of this particular event Figure 5 c. Initially, this anomalous trend is observed 5 days prior to the Wenchuan earthquake on May 07, , and continued until July showing post earthquake anomalies.

It is important to mention that these post earthquake changes were possibly associated with the aftershock sequence of the Wenchuan earthquake [ 50 ].

Similarly, the SPS monitoring station having and shows a multifold increase in the daily Rn levels around the time of the Wenchuan earthquake Figure 3 d. The average Rn value recorded at the SPS monitoring station ranges between 0. Additionally, the residual Rn level of the SPS monitoring station overpasses the anomaly selection threshold which further authenticates its linkage with this particular event.

The GS monitoring station reveals an unambiguous increase in the Rn levels prior to event occurrence within a very short interval of time days. Afterwards, a sharp increasing trend of Rn is observed from the beginning of May and continued onward Figure 3 e.

This anomalous change is subject to detailed analysis via residual signal processing technique for reliable identification of tectonically induced Rn anomaly. The results of the residual Rn show development of an Rn anomaly overpassing the anomaly selection threshold preceding the earthquake event Figure 6 a. Similarly, the other monitoring stations presented anomalous patterns of the daily and the residual Rn levels highlighting the aspect of tectonically induced Rn anomalies under favourable conditions Figures 3 f , 3 g , 6 b , and 6 c.

Inclusively, all the monitoring stations lying around the proximity of the LMSF zone show Rn variations with varying amplitude in connection with this particular event.

At the second stage, the temporal variability of Rn is analysed for distant Rn monitoring station with Figures 3 — 6. The temporal variability of raw Rn levels recorded at these monitoring stations shows an increasing trend around the time of the Wenchuan earthquake, despite its distant location Figures 3 h and 3 i. The highest peak Likewise, the SYS station also shows an analogous change in the Rn levels around the time of this particular event Figure 3 i.

These anomalous periods are further subjected to a detailed investigation as presented in Figures 6 a and 6 b. The residual Rn value of the YQS station shows a significant increase on April 30, , and overpasses the statistical criterion of the anomaly selection.

This anomalous change in residual Rn is followed by the Wenchuan earthquake that occurred 11 days later, while during the rest of the period Rn levels were found to be within normal limits Figures 3 h and 7 a.

On the contrary, the temporal analysis of the residual Rn at the SYS station also depicts a notable rise in Rn levels almost days prior to this devastating earthquake Figure 7 b. Moreover, the comparative analysis of rainfall for the whole year and selected days further confirms the connection of Rn abnormality with this particular event Table 2. It is important to mention here that the current results show an analogy with the earlier investigations of the preseismic process in connection with the Wenchuan earthquake [ 8 , 12 , 51 , 62 ].

For example, Shi et al. Radon diffuses out of the earth in small, variable quantities all the time, but these can increase when reductions in pressure allow radon or fluids carrying it in solution to escape to the surface.

Such pressure drops can accompany — or precede — the shearing of rocks in an earthquake. Answer from: liliumflower I guess that the use of radon is that it will help diffuse out the Earth but it can increase the reductions in pressure which is helpful in an earthquake.

The solution will escape to the surface as its pressure will drop to stop an minimize the magnitude of an earthquake. Answer from: student Radon diffuses out of the earth in small, variable quantities all the time, but this can increase when reductions in pressure allows radon or fluids carrying I. Solution to escape to the surface, such pressure drops can accompany or precede the sharing of rocks in an earthquake!

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