Jae-Kyoung Lee

Innovation Center for Engineering Education, Daejin University, Pocheon-si, Gyeonggi-do, 487-711, Republic of Korea

Abstract: Following the Pohang and Gyeongju earthquakes and their aftershocks, there is no longer any zone that is safe from earthquake-related disasters in the Korean Peninsula. In order to monitor and predict earthquakes, correlation analysis of earthquakes and hydro-environmental factors are insufficient, and the development and application of hydro-environmental factor measurement equipment is still in the early stages. This study developes and verifies a more precise radon measurement device. Four specific earthquake cases (2019–2020) were selected, and the correlation of the analyses of the earthquakes and hydro-environmental factors (radon, electric conductivity (EC), water-level (WL), and water-temperature (WT)) was conducted at the three specific groundwater stations. Accordingly, was confirmed that four factors are affected by earthquakes or seismic movement. Furthermore, the variability of the EC showed an identical tendency for a certain period before an earthquake occurred, and, in particular, the variability trends for radon, WL, and EC coincided at the time of the earthquake′s occurrence.

Keywords: variability analysis; earthquake; radon; hydro-environmental factor

1 Introduction

Overall, heavy rain, typhoons, and droughts are among the main causes of natural disasters on the Korean Peninsula. In particular, few experts are interested in earthquake risks, as earthquakes and their resultant damage are relatively infrequent. However,917 earthquakes with more thanML2.0 occurred during the period from 2008 to 2017, and there were 223 earthquakes in 2016 and 252 earthquakes in 2017, which together represent five times the annual average of the 50 earthquakes that occurred from 2008 to 2015 (Kimet al.,2021). Moreover, Gyeongju, Gyeongbuk (September 12,2016) experienced the largest earthquake, measured atML5.8, and Pohang, Gyeongbuk (November 15, 2017)suffered a particularly large earthquake, measuringML5.4. Additionally, the aftershocks caused by the Gyeongju and Pohang earthquakes occurred more than 400 times. As the magnitude and frequency of these earthquakes have gradually increased over time, there is no longer any zone that is safe from earthquake disasters on the Korean Peninsula. Although the government has established various earthquake disaster preparedness policies in the aftermath of the Gyeongju and Pohang earthquakes, relevant policies have been focused on the evacuation and management of earthquakes that have taken place, and additional fundamental measures for early prediction of and preparation for earthquakes have come into use (Wooet al., 2020).

Many studies have been conducted to better predict earthquakes and assess their various causes (including tectonic motion, surface deformation, slope and stress),as well as changes in water environment factors such as groundwater level, groundwater chemical composition,radon, pH, aquifer composition, hydrogen isotopes of water molecules, and various water quality-related variables. Furthermore, studies have focused on the correlation of earthquake-related factors with earthquake precursors and post-earthquake changes (Kitagawa and Matsumoto, 1996; Chiaet al., 2001; Cartignyet al., 1995; Kitagawaet al., 2006; Chadhaet al., 2008;Miklavčičet al., 2008; Chenet al., 2014; Tsunomori and Tanaka, 2014; Gotoet al., 2017; Xionget al., 2020;Raushan and Ajay, 2021; Abdullabekov and Yusupov,2022). Rikitake (1979) distinguished nineteen precursor factors (including surface deformation, electrical conductivity fluctuations, groundwater fluctuations, etc.)pertaining to earthquake occurrence, and these results were used to analyze the magnitude of earthquakes and the predictability of the time of their crustal changes prior to their occurrence. Tiwariet al. (2004) found that the majority of all earthquakes in India occurred mainly during or immediately after monsoons, and that there was a correlation between rainfall and earthquake occurrence.He also revealed that it is possible to predict earthquake occurrence by means of rainfall pattern analysis. Yanet al. (2014) analyzed the correlation between the changes in groundwater level, distance from a quake′s epicenter,and the depth of various observers located in China with regard to the influence of the 2011 Tohoku earthquake(M9.0) that took place in Japan.

Among domestic studies, Kimet al. (2003) analyzed the relationship between earthquake occurrences and radon by continuously monitoring the incidence of the concentration of radon within the atmosphere and groundwater. KMA (2010) installed a measuring instrument that could measure radon in the air and in seawater that existed underground within the Uljin area,subsequently observing changes in radon that were caused by earthquakes. Joenget al. (2011) investigated the changes in groundwater level in Korea following theML9.0 (by use of Richter local magnitude) earthquake occurrence in Japan, and thus revealed correlations with hydraulic conductivity, the permeability coefficient,groundwater level changes, and earthquake occurrences.Wooet al. (2015) analyzed the correlation between fluctuations in groundwater level, water temperature,and electrical conductivity of groundwater as a means of gathering predicting precursors to the occurrence of small-scale earthquakes (withinML3.0) with high frequency by utilizing long-term observed data taken from groundwater stations. Lee (2016) and Kimet al. (2018) analyzed the variability of groundwater levels by measuring radon in the national groundwater station resulting from the effects of the 2016 Gyeongju earthquake. In summary, although these existing studies deal with the possibility of earthquake occurrence being linked to changes in hydro-environment factors(groundwater quality, pH, groundwater chemical composition, groundwater level, radon, electrical conductivity, etc.), this analysis of various earthquake cases′ relationship to hydro-environmental factors is insufficient, and in particular, studies for the development and application of hydro-environmental factor measurement equipment is still in the early stages.

The purposes of this study are as follows: First,this study aims to examine recent trends through an analysis of various earthquake occurrence cases that have taken place on the Korean Peninsula. Second,this study develops a radon measurement device that can accurately measure radon generated by crustal movement. Finally, this study analyzes the correlations and trends between and among earthquake occurrence and hydro-environment factors during space and time by including observed radon data combined with the use of a developed radon measurement device. Hence, this study provides the basic data for earthquake occurrence prediction.

2 Trend analysis of recent earthquakes

2.1 Application data

This study used national-level data to analyze the trend of recent earthquake cases that occurred nationwide throughout the Korean Peninsula. The national-level data included records (from 1978 to 2020, involving such factors as depth, latitude and longitude, magnitude,etc.) from a total of 206 earthquake monitoring stations provided by Korea Meteorological Administration(KMA), observed data (from 2009 to 2020, including groundwater level, groundwater temperature, and electrical conductivity) from 412 groundwater monitoring stations in the Groundwater Information Center (GIMS),and area-average precipitation data (from 1978 to 2020)taken from Water Resources Management Information System (WAMIS), as shown below in Table 1.

2.2 Recent earthquake trend analysis

When including South and North Korea, the number of earthquakes totaled 1,964, from 1978 to 2020. The number of earthquakes ofMLranging from 2 to 3 was the most frequent, occurring 1,545 times (78.7%), while earthquakes of magnitude 5.0 or larger took place only eight times (0.4%). On the Korean Peninsula, of 1,320 total earthquakes, only the inland earthquakes (those with an earthquake epicenter situated on land) and undersea earthquakes (earthquakes with an earthquake epicenter in the ocean) were examined in greater detail.In all, undersea earthquakes occurred 733 times, about 1.5 times more than the inland earthquake (587 times).Both inland and undersea earthquakes, with anMLranging from 2 to 3, occurred the most frequently, with 480 (81.8%) and 554 (75.6%), respectively, as shown in Table 2.

Through an analysis of the trend of earthquake cases with respect to region and time, the results of inlandearthquakes classified by administrative districts show the rate of earthquake occurrences ofMLranging from 2 to 3 was the highest (81.8%) among all the administrative districts. Among the administrative districts, the Gyeongbuk region (located in the southeastern region of the Korean Peninsula) had the greatest number, with a total of 248 (42.2%), followed by the Chungnam region(the southwestern region—79 in all), the Gyeongnam region (the southeastern region—51 in all), and the Gangwon (the northeastern region—43 in all).

Table 1 Summary of earthquake basic data

The cause of the largest number of earthquakes in the Gyeongbuk region was analyzed in relation to the effects of an earthquake and its aftershocks, which occurred in Gyeongju on September 12, 2016, and an earthquake and aftershocks, which happened in Pohang on November 15, 2017. In the period from September 2016 to December 2017, 61 earthquakes occurred.According to the magnitude of the earthquake that took place in the Gyeongbuk region in Fig. 1(a), after the Gyeongju earthquake case, the number of earthquakes increased dramatically over time due to the effects of the aftershocks. In addition, the intervals between earthquakes occurred after the aftershocks in the case of the Pohang earthquake. However, as shown in Fig. 1(b),earthquakes rarely happened the Gyeongnam region due to the minimal effects of the Gyeongju and Pohang earthquake cases.

Undersea earthquake trends were analyzed by geographically dividing the cases among three seas(East sea, West sea, and South sea), and the earthquakes withMLfrom 2 to 3 occurred most frequently among all of them, totaling 554 (75.6%). Earthquakes took place most frequently in the West sea, with a total of 328(44.7%), followed by the East sea, with 240 (32.7%) and finally South sea, with165 quakes (22.5%), as shown in Table 3. The reason for the majority of the earthquakes occurring in the west sea can be traced to two major earthquakes that took place to the south of Baengnyeong island in Incheon in May 2013 (ML4.9) and in the waters southwest of Boryeong, Chungnam, in June 2013 (ML3.5), along with their subsequent aftershocks. Figure 2 reveals that the earthquakes in the West sea occurred more frequently than in the East sea, and that the larger the magnitude of the earthquake, the greater the proportion of the energy of the seismic waves.

The analytical results of the earthquakes by year reveal that a relatively greater number of earthquakes occurred in 2013, as shown in Fig. 3(a). In particular,earthquake occurrence increased most sharply after the 2016 Gyeongju and 2017 Pohang earthquakes. As shown in Fig. 3(b), when dividing the earthquakes into inland and undersea quakes, while the major reasons for the earthquakes in 2013 can be traced to the impacts of the undersea earthquakes, the quakes that occurred after 2016 were largely caused by inland earthquakes.Moreover, due to the influence of inland earthquakes,the overall frequency of undersea quakes also showed a tendency to increase. According to month, the majority of earthquakes occurred during September(12.9%) across the entire Korean Peninsula, followed by November (10.2%) and June (9.0%). It was observed that the Gyeonju (September) and Pohang (November)earthquakes and their aftershocks led to the rate of earthquake occurrence increasing for their corresponding months.

For the baseline statistical analysis, the values of the undersea earthquakes were greater than for the inland earthquakes regarding average earthquake magnitude(ML2.71), median earthquake magnitude (ML2.6), and the number of earthquakes (733), but the maximummagnitude of the inland earthquake was higher than that of the coastal earthquake, as shown in Table 4. In terms of the statistics of earthquakes with anMLof more than 4.0,inland earthquakes demonstrated the greatest values in terms of average and median earthquake magnitude (ML4.75 and 4.7, respectively), but the number of undersea earthquake occurrences was analyzed and diagnosed as“frequent”.

Table 2 Summary of earthquake occurrences on the Korean Peninsula

Fig. 1 A comparison of the earthquake time series in Gyeonguk and Gyeongnam

When taking into consideration the number of earthquakes by month, as shown in Fig. 4(a), the inland earthquakes tended to occur most often during the spring and winter, and the number of undersea earthquakes occurred most frequently from April to June. In particular, when taking into consideration the relation of the occurrence of inland and undersea earthquakes, the onset of monthly data for undersea earthquakes delayed by two months reveals that the occurrence tendency of inland and undersea earthquakes was very similar,with the correlation coefficient (R2) calculated to be as high as 76.3%. According to these results, the effect of inland earthquakes was one of main reasons for undersea earthquakes occurring two months later. This research topic should undergo greater analysis in future studies.

Table 3 A summary of the earthquake occurrences by each seaside

Table 4 The statistics for inland and undersea earthquake occurrences

Fig. 2 A comparison of the magnitude and the seismic energy of earthquakes in the East and West sea

Fig. 3 The annual earthquake occurrences of all, inland, and undersea

3 An analysis of earthquake occurrence and hydro-environmental factor variability

3.1 A development of radon measurement devices based on NB-IoT

Earthquake occurrences or seismic waves are affected by a wide variety of hydrologic, chemical, and electrical variables related to the crust, including: (1)precipitation infiltrating underground, which then affects the crust; (2) the variability of groundwater level (WL)and groundwater temperature (WT); (3) the variability of electric conductivity (EC) in the groundwater; (4)changes in radon concentration from the crust to the ground; and (5) changes in natural radioactive materials within groundwater. The variability of groundwater level is normally likely to be affected by precipitation or groundwater usage—factors related to water-quality(radon, alkalinity, EC, pH, and so on) that are more affected by crustal anomalies.

This study focuses specifically on radon measurement, which is relatively easy to measure in most cases, and, by using an existing groundwater station for this purpose, this study developed more robust, powerful radon measurement devices.

The specific methods for measuring the radon concentration within groundwater includes the alpha track detection method, the charcoal canister method,the electretion chamber method, the pulsed ionization chamber method, the scintillation cell method, and a variety of others. Among existing measurement methods,the most frequently applied one is to measure the beta and gamma rays within the radon concentration in groundwater or within air vaporized from groundwater,but the limitation of this method is that the beta and gamma rays tend to scatter very quickly in the air. In order to overcome this limitation, this study dealt with a method for detecting the alpha particles in the air that were not beta or gamma rays, which are relatively easy to measure, followed by an analysis of the particles within the radon vaporized in groundwater. Measuring the radon directly in groundwater was excluded as a method in this study as it could actually affect the groundwater observation equipment′s efficacy or the accuracy of the groundwater observation record.

The particular radon measurement device used, as shown in Fig. 5, was composed of a radon measurement sensor block, a control block, and a narrow broadband(NB)-internet of thing (IoT) block. In addition to the ability to send a control command, the radon sensor block and NB-IoT block transmit observation data or can control commands through the control block and universal asynchronous receiver transmitter (UART)or the universal serial bus (USB) interface. The radon measurement sensor block also serves to monitor the overall radon observation data and the status of the observation device. Additionally, the NB-IoT module block plays an essential role in sending and receiving the observation data. Further, the control block plays a role in systematically collecting and processing the data and organizing it into a reliable form. This study then conducted various system interlocking tests as to whether any radon data were transmitted successfully through the developed device. Table 5 shows that the radon measurement sensor and the NB-IoT block configuration and device interwork.

Fig. 4 The correlation of inland earthquakes with undersea earthquakes by delaying 2 months of undersea earthquake monthly data

Fig. 5 The composition of the radon measurement device based on NB-IoT

3.2 Field test operation of the radon measurement devices

The radon measurement methods for the groundwater wells can be largely divided into the in-well measurement method, the above-well measurement method, and the side-well measurement method. The in-well measurement method has the advantage of being able to directly measure the amount of radon vaporized within the groundwater, but there are significant disadvantages for this method, such as the need to maintain a constant,significant separation distance between the groundwater and the radon sensor, the increase in costs due to the production of waterproof modules, the possibility of collision with the existing water-level sensor, and the need to secure the inner diameter of the well. The overall reliability of the above-well measurement method is slightly lower than that of the radon measurement within the well, and the observed radon concentration may thus be affected by the groundwater level. However, the abovewell measurement method is still useful for measuring the level of radon change, and there is no probability of collision with the radon measurement sensor and the water-level sensor. The side-well measurement method also presents similar advantages for the above-well measurement method, but the measured data gathered when this method is employed shows a lower level of reliability than is the case for the two previously mentioned methods due to the indirect measurement of the radon concentration.

Within this study, the Gimpo Haseong and Gimpo Wolgot groundwater stations were selected as the field test sites, along with the cooperation of K-water, which actually manages and operates groundwater stations. The reasons for selecting these two stations were finalized when taking into consideration the accessibility,measurement method, convenience of power supply, and the specific ease of installing the measurement devices at the time of field test operations. Additionally, the radon measurement devices that were developed in this study were installed and operated within two stations,respectively. Table 6 shows the overall basic information,installation location, and surrounding environments of the Gimpo Haseong and Wolgot groundwater stations.

When analyzing the results of the field test operations, the field test operation period was carried out for a period of around 3 months from May to July 2019, and it was confirmed that the radon observed data was measured consistnetly and accurately at the Gimpo Wolgot and Gimpo Haseong groundwater stations.In Fig. 6, during the field test operation period, the groundwater level in the Gimpo Wolgot station showed a tendency to be slightly lower, but the EC and radon measurement values did not show actual specific trends since the earthquake or seismic movements did not occur very near the field test. In particular, it was confirmed that the radon measurement device which developed over the course of this study can be an appropriate tool to measure the radon concentration within the field sites, as these devices transmitted the radon measurement values to the database well, and the quality of measurement data was also high.

3.3 Correlation analysis between earthquake occurrences and the variability of hydroenvironmental factors

In this study the radon measurement devices were compared to the devices used during the field test operation, and additional verification was performed by means of a selection of the test bed, which focused on Gyeongbuk Pohang, which had the highest rate of earthquake occurrences. For the period from December 2019 to May 2020, with the cooperation of K-water,NB-IoT radon measurement devices were installed at the Pohang Singwang, Pohang Jangheung, and Pohang Donghae groundwater stations.

This study focused on earthquakes, so corresponding data from events that happened after November 2019 were used to analyze the variability of earthquake occurrences and hydro-environmental factors in orderto analyze daily radon data, which was measured via the use of developed radon measurement devices, with daily observed WL, EC, and WT. Table 7 shows that the earthquake cases that occurred were selected through seismic observation networks that were operated by the Korea Meteorological Administration. Figure 7 shows the specific epicenter locations of the earthquakes and the NB-IoT radon measurement devices.

Table 5 A description of the radon measurement device structure

Fig. 7 The locations of radon measurement stations and the earthquake occurrence cases

Fig. 8 The time series of observed WL, EC, WT, and radon in the groundwater stations; (a)&(b): Donghae, and (c)&(d): Singwang stations for the E1 case

Figures 8 to 11 show the specific time series of the WL, EC, WT, and radon measurement data at the Pohang Shingwang, Pohang Donghae, and Pohang Jangheung groundwater stations. Figure 8 reports on an earthquake that occurred near Miryang-si on December 30, 2019, with anMLmeasuring 3.5, in addition to the time series of the four observed data before and after the earthquake. Figures 8(a) and 8(c) show that there was a dramatic decrease in the WL that appeared before the time of the earthquake occurrence, and that the width of the EC greatly fluctuated within the E1-1 and E1-3 sections. Additionally, in Figure.rall tendency to increase or decrease around the specific time of the earthquake occurrence, and the overall variability of the WT in the E1-2 section appeared for a significant period of time. Overall, Figure 8(d) also shows that the radon concentration slightly increased before the onset of the earthquake. Moreover, it was also found that there were no significant external causes, other than the earthquake,as the rainfall amount (December 29-30, 2019: 6.0 mm,Pohang weather station) at the time of the earthquake did not have a significant effect. Figure 9 shows the time series of the four observed data around the earthquake,which occurred near Sangju-si on January 30, 2020, with anMLmeasuring 3.2. As shown in Figs. 9(a) and 9(c),a significant increase in WL appeared before the time of the earthquake occurrence could be noted, and the width of EC greatly fluctuated within the E2-1 and E2-3 sections. Additionally, as seen in Figs. 9(b) and 9(d),the radon concentration at the Pohang Singwang station took place with a tendency to increase overall, but the radon values of the Donghae station tended to decrease over time. While the variability of the WT in the E2-2 and E2-3 sections appeared for a period of time, the WT values were shown to be consistent following seismic movement. Moreover, it also was found that there were no actual external causes other than the earthquake itself, as the rainfall amount (January 27, 2020: 4.7 mm,Sangju weather station) at the time of the earthquake did not have any significant effect.

Fig. 10 The time series of the observed WL, EC, WT, and radon in the groundwater stations; (a)&(b): Donghae, and (c)&(d): Singwang stations for the E3 case

In sequence, as displayed in Fig. 10, the earthquake in the East sea took place 102 km from Yeongdukgun on February 16, 2020, with anMLof 2.7, and a time series of four observed data before and after that earthquake occurred are shown. As pictured in Figs.10(a) and 10(c), the WL values decreased slightly before that specific earthquake, and the EC values significantly decreased and then increased, thereby resulting in huge fluctuations within the E3-1 and E3-3 sections.Additionally, as can be seen in Figs. 10(b) and 10(d),the radon concentration at the Pohang Jangheung and Donghae stations increased overall, and the values of WT in the E3-2 and E3-4 sections varied slightly for the given time period. Moreover, it was also noted that there were no other significant external causes, other than the earthquake, as the rainfall amount (February 15-16, 2020: 2.7 mm, Yeongduk weather station) at the time of the earthquake did not have any effect on the outcome. Specifically, Fig. 11 shows the time series of the four observed data in regard to the earthquake that occurred near Kimcheon-si on May 27, 2020, which had anMLof 2.8. In Figs. 11(a) and 11(c), the width of the WL dramatically fluctuated within the E4-1 section,while the values of the EC are shown constantly, and,as shown in Fig. 11(c), the WL significantly decreased and the EC varied greatly within the E4-2 section prior to the earthquake′s occurrence. Additionally, and as seen in Figs. 11(b) and 11(d), the radon concentration at the Pohang Singwang and Donghae stations occurred with a stronger tendency to increase, and while the values of the WT were continuing to fluctuate before the earthquake,the WT was shown to be consistent in the aftermath of the earthquake. In particular, in illustrated in Fig. 11,the trend of hydro-environmental factors was examined through an extension of the period to the past by 30 days.Within an extended period, wherein these factors showed a certain specific trend without large fluctuations, the tendency of these factors varied significantly, displaying the largest fluctuations from an average of 10 to 20 days before the earthquake. Moreover, it also was determined that there were no external causes present, other than the earthquake, as the rainfall amount (May 24, 2020:0.6 mm, Sangju weather station) at the time of the earthquake did not have a significant effect.

Fig. 11 The time series of the observed WL, EC, WT, and radon in the groundwater stations; (a)&(b): Donghae, and (c)&(d): Singwang stations for the E4 case

Table 7 A description of the earthquake occurrence cases

3.4 Temporal trend analysis of the variability of hydro-environmental factors as a result of earthquake occurrence

In accordance with the research results presented in the previous section, it was confirmed that the hydroenvironmental factors of the groundwater stations fluctuated most significantly around the times of their corresponding earthquake occurrences, and certain patterns of hydro-environmental factors related to the earthquakes also were analyzed. In particular, for an additional earthquake case that occurred near the Pohang weather station, the tendency of the hydroenvironmental factors (radon, WL, EC) according to their corresponding earthquake was analyzed by means of a temporal expansion. During the time period from November 2019 to November 2020, certain earthquakes were selected as cases that occurred in close spatial proximity to the center of the Pohang weather station(within 65 km), as shown in greater detail in Table 8.Overall, this study′s contributions reveal that the temporal variability analysis still has some limitations in its robustness, as it provides only a base study for the overall monitoring of earthquake occurrence and the prediction of future earthquake events.

For these three earthquake cases, the previous analysis was also carried out in a similar manner, specifically focusing on the observed data gathered from the three groundwater stations. The analytical results are shown in Table 9. For the case of the temporal analytical results,the trends of WL decreased (changes in amount: more that EL. 0.04 m) at the same three stations for an average of four to two weeks before the earthquake occurred, and the WL showed a tendency to increase from a period of two weeks to one day before the earthquake took place(changes in amounts: greater than EL. 0.06 m). In the cases of radon and EC, the tendencies to increase and decrease changed according to factors present in the period before the earthquakes occurred.

Based on the temporal analytical results, this study analyzed the coincidences of the variability trends of radon, EC, and WL variability at all three groundwater stations prior to earthquake occurrences, for a total of three cases. The meaning of “coincidence of variability trend” in this study indicates that the variability trends(both increasing and decreasing trends) of the hydroenvironmental factors of the Pohang Shingwang, Pohang Donghae, and Pohang Jangheung groundwater stations occurred simultaneously.

For this study, the daily variability trends of radon,WL, and EC were analyzed according to the specific earthquake occurrences taking place during the analysis period, as shown in greater detail in Table 10 and Fig. 12. In Table 10, the specific symbols in each column indicate the identical variability trend (for increasing and decreasing trends), respectively. For example, the circles in the WL columns for two days indicate that the variability trends of WL were identical during the course of the two days. In the results stemming from the analysis of the variability trend, as shown in Table 10, the variability of EC revealed analogous trends up to 10 days prior to the earthquake occurrence in all three cases. Specifically,the variability trends of radon, WL, and EC appeared simultaneously at the time of the earthquake. As can be seen in Fig. 12, the variability trends of radon, WL, and EC coincided at a greater rate than once in Box1, Box2,and Box3 for a given time period, whereas before, the occurrence of the earthquake coincided with at least one tendency. Moreover, as displayed in Fig. 12(b), prior to the earthquake, the variability of WL and radon showed on average an identical tendency from about fifteen days ago before the occurrence of the earthquake, and EC showed an identical variability tendency for a period of about seven days before earthquake occurrences.

Table 8 A descriptive table of earthquake occurrence cases for variability coincidence analysis

Table 9 The trend analysis results of radon, WL, and EC variability before the earthquakes′ occurrences

When summarizing the core results of this study,it is important to note that when monitoring hydroenvironmental factors of groundwater stations, if the precise variability of hydro-environmental factors shows identical trending patterns, or if the variability trends of all factors occur simultaneously for a certain period prior to the occurrence of earthquakes, this indicates that earthquakes are more likely to occur over a short period of time. Therefore, the monitoring and analysis of the variability trends of hydro-environmental factors constitute the core, basic data required for earthquake occurrence monitoring and prediction.

Table 10 The trend coincidence analysis of radon, WL, and EC for 3 specific earthquake cases

Fig. 12 A figure of the time series of radon, WL, and EC coincidence analysis for 3 earthquake occurrence cases: (a) number of coincidences of three factors, (b) coincidences between EC and WL

4 Discussion

This study revealed the correlation between four major factors and earthquake occurrences. However,there are limits to improving earthquake occurrence monitoring and prediction, as follows. A prominent limitation of this study, is that first, only the variability and the trends of the hydro-environmental factors were analyzed, and the study for crustal movement and composition was insufficient overall in providing a robust analysis. Given that crustal and stratum movements are major, impactful reasons for predicting earthquakes—and therefore necessarily affect hydro-environmental factors—a far more accurate analysis of the correlation pattern between earthquakes and hydro-environmental factors would be possible if additional studies of crustal motion are conducted. Second, there is a limitation in the use of hydro-environmental factors for analysis, as in addition to the hydro-environmental factors that were used in this study, other various hydro-environmental and chemical factors have been widely used in previous studies. However, for this specific study, the factors were not applied widely due to challenges imposed by data acquisition limitations. In the future there should be a greater variety of observed data taken from a higher number of groundwater and weather stations, with more detailed information about hydro-environmental and chemical factors, in order to more robustly analyze the correlation between earthquakes and the factors that correspond to their occurrence.

5 Conclusion

In conclusion, this study analyzed the various relevant trends of the earthquake occurrences, as well as the correlations and variability observed between hydroenvironmental factors and earthquake occurrences.Moreover, this study widely improved upon the accuracy of correlation analysis between hydro-environmental factors and earthquake occurrences through the application of a novel radon measurement device used in this study. Researchers compared observed hydroenvironmental data with other major earthquake cases as follows.

First, this study analyzed a dataset containing information on the earthquake cases in Korea (1978 to 2020) by dividing them according to region and time, as well as by categories of inland and undersea earthquakes.In terms of geographic region, the Gyeongbuk region(42.2%) and the West sea (44.7%) had the highest rate of earthquakes, and the Gyeongbuk region was found to be greatly affected in particular by the Gyeongju(September 12, 2016) and Pohang (November 15,2017) earthquakes. According to the results recorded time, the occurrence rate (12.9%) of the earthquake in September measured the highest, and this case also was analyzed and diagnosed as having an effect of the Gyeongju earthquake. Among the inland and undersea earthquakes, the earthquake correlation between the occurrence of the undersea quakes lagged by an average of two months, and the figure for inland earthquakes was high (R2= 76.3%).

Second, this study developed and tested a novel radon measurement device. The new radon device was used to test and verify at two specific groundwater stations over a period of three months in a series of field test operations. In the next test-bed operations,the correlation of the analyses of the earthquake occurrences and hydro-environmental factors (Radon data taken from the new device, and EC, WL, and WT from the observed stations) was conducted at the three groundwater stations for four specific earthquake cases happening in 2019 and 2020. For the analytical results, although there is a distinct difference between each groundwater station, it was observed that the actual variability of the hydro-environmental factors dramatically fluctuated (showing a tendency to increase or decrease at different intervals) at a certain time period around the moment when the earthquakes occurred. It was thereby confirmed that radon, WL, WT, and EC are affected by earthquake occurrences or the presence of seismic movement, and this study proved that that there is a distinct correlation between earthquake occurrences and hydro-environmental factors.

Finally, in order to further analyze the trends of selected hydro-environmental factors, an in-depth temporal trend analysis of the variability of all factors according to earthquake occurrence was carried out. As a result, the variability of the EC showed an identical tendency over a certain period before an earthquake occurred, and, in particular, the variability trends for radon, WL, and EC coincided at the time of the earthquake′s occurrence.

Therefore, the monitoring and analysis of the variability trends and the correlation of hydroenvironmental factors have the potential to support the most essential basic data for earthquake occurrence monitoring and prediction. It is expected that the overall accuracy of earthquake monitoring and prediction can be improved and may become more robust through detection of overall fluctuations caused by hydroenvironmental factors due to earthquake occurrences in the near and far future.

Acknowledgment

The Institute of Engineering Research at Daejin University provided research facilities for this work.