Next Article in Journal
Design of a Digital Array Signal Processing System with Full Array Element
Next Article in Special Issue
Horizontal Magnetic Anomaly Accompanying the Co-Seismic Earthquake Light of the M7.3 Fukushima Earthquake of 16 March 2022: Phenomenon and Mechanism
Previous Article in Journal
Pseudolite Multipath Estimation Adaptive Mitigation of Vector Tracking Based on Ref-MEDLL
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 15
Issue 16
10.3390/rs15164042
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Analysis of Lithosphere–Atmosphere–Ionosphere Coupling Associated with the 2022 Luding Ms6.8 Earthquake

by
Jiang Liu
1,
Xuemin Zhang
2,*,
Xianhe Yang
1,
Muping Yang
3,
Tiebao Zhang
1,
Zhicheng Bao
4,
Weiwei Wu
1,
Guilan Qiu
1,
Xing Yang
1 and
Qian Lu
1
1
Sichuan Earthquake Agency, Chengdu 610041, China
2
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
3
Liaoning Earthquake Agency, Shenyang 110034, China
4
Jiangxi Earthquake Agency, Nanchang 330096, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(16), 4042; https://doi.org/10.3390/rs15164042
Submission received: 13 July 2023 / Revised: 10 August 2023 / Accepted: 14 August 2023 / Published: 16 August 2023
(This article belongs to the Special Issue State of the Art of Geomagnetic/Electromagnetic Satellites: Science and Applications)
Browse Figures
Versions Notes

Abstract

:
Taking the Luding Ms6.8 earthquake (EQ) on 5 September 2022 as a case study, we investigated the potential seismic anomalies of the ionosphere, infrared radiation, atmospheric electrostatic field (AEF), and hot spring ions in the seismogenic region. Firstly, we analyzed the multi-parameter anomalies in the ionosphere around the epicenter and found synchronous anomalous disturbances in the ground parameters, namely the global ionospheric map (GIM), GPS, TEC, and satellite parameters, such as the He+ and O+ densities on 26 August under relatively quiet solar–geomagnetic conditions (F10.7 < 120 SFU; Kp < 3; Dst > −30 nT; |AE| < 500 nT). Next, both the anomaly analysis of the infrared radiation and AEF, and the survey results of the Luding EQ scientific expedition on the hot spring ions showed pre-seismic anomalous variations at different time periods in the seismogenic region. The characteristics of Earth’s multi-sphere coupling anomalies in temporal evolution and spatial distribution were obvious, which validated the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism. Finally, combining the analysis results and the LAIC mechanism, we suggested that the multi-sphere coupling anomalies were more likely associated with the Luding Ms6.8 EQ, and that the differential motion and the regional crustal stress accumulation between the Chuandian block and the Bayan Har block might have led to this EQ. Furthermore, remote sensing and ground-based monitoring technologies can play an important role in corroborating and compensating each other, while further study of the multi-sphere coupling mechanism will provide a clearer understanding of the seismogenic process for major EQs.

1. Introduction

In recent decades, seismologists have paid increasing attention to the study of the relationship between dynamic changes in the lithosphere and ionospheric perturbations preceding large earthquakes (EQs) [1,2,3,4]. Many scholars believe that the electromagnetic signals associated with EQs can propagate upward from the lithosphere to the atmosphere and even to the ionosphere, with geochemical, acoustic, and electromagnetic pathways as the primary modes of propagation. Subsequently, the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism was intensively developed to study the Earth’s multi-sphere coupling anomalies during EQ incubation and development [5,6,7,8,9]. Seismic electromagnetic signals propagate from the crustal gestation zone to the ionosphere through a complex coupling process, spanning multi-sphere structures and triggering anomalous disturbances in infrared radiation, the atmospheric electrostatic field (AEF), and the ionosphere. The joint analysis of ground-to-space observations will provide a clearer understanding of the seismic anomaly coupling processes in the Earth’s multi-sphere structures [10,11,12].
Multiple previous studies on seismic events have been conducted and numerous pre-seismic anomalies in infrared radiation, the AEF and the ionosphere have been identified and reported. Firstly, the survey results of the infrared radiation anomalies showed that the persistent enhancement of radiation in the phase before a large EQ may reflect the continuous accumulation of regional stress, which could eventually lead to the occurrence of an upcoming main shock in the seismogenic region [13,14,15,16,17,18]. For example, Barkat et al. [13] utilized the moderate resolution imaging spectroradiometer (MODIS) imagery data for a precursory analysis of three major EQs, including the Kashmir Mw 7.6 EQ on 8 October 2005, the Ziarat Mw 6.4 EQ on 28 October 2008, and the Dalbandin Mw 7.2 EQ on 18 January 2011. Their results showed a significant correlation between land surface temperature (LST) anomalies and seismic activities. An increase of 3–10 °C in LST was observed 6, 4, and 14 days, respectively, prior to the Kashmir, Ziarat, and Dalbandin EQs. Meanwhile, based on the analysis results from the Wenchuan Ms8.0 EQ in 2008, the Lushan Ms7.0 EQ in 2013, and the Jiuzhaigou Ms7.0 EQ in 2017, Jing et al. [16] illustrated that microwave brightness temperature (MBT) anomalies all appeared approx. two months before the three EQs. Similarly, Zhang et al. [18] studied the features of low-frequency information in infrared radiation before and after the Wenchuan Ms8.0 EQ in 2008, the Lushan Ms7.0 EQ in 2013, the Jiuzhaigou Ms7.0 EQ in 2017, and the Maduo Ms7.4 EQ in 2021. It was found that all of them showed a radiative enhancement phase before the EQ and a radiative weakening phase after the EQ, which could indicate that the rock stress loading caused an increase in temperature and the unloading caused a decrease in temperature.
Secondly, as an important part of the global circuit, anomalous changes in the atmospheric electric field have attracted the attention of seismologists, and atmospheric disturbances caused by seismic signals have also been reported frequently [19,20,21,22,23,24]. For example, Smirnov [19] statistically analyzed one hundred and three cases of a bay-like depression of the AEF intensity in the near-surface atmosphere, which were observed in Kamchatka from 1997 to 2002. The results showed that the duration of a bay-like depression was typically 40–60 min, and the probability of an EQ prediction within 24 h was approx. 36%. Meanwhile, Chen et al. [23] proposed a physical mechanism for pre-seismic AEF anomalies using EQ case studies and suggested that the abnormal signals might be related to the occurrence of EQs under fair weather conditions. In addition, based on the analysis of the AEF observations in Sichuan province, China, Jin et al. [24] found that synchronous negative anomalies in the AEF were observed in the EQ preparation zones before major EQs, such as the Wenchuan Ms8.0 EQ in 2008, the Jiuzhaigou Ms7.0 EQ in 2017, and the Changning Ms6.0 EQ in 2019, which indicated that synchronous anomalies in the AEF might be an important precursor signal of an upcoming main shock.
Finally, seismic precursor signals propagate upward from the atmosphere to the ionosphere under certain conditions and cause the abnormal disturbances in the ionospheric multi-parameter, which is known as the seismo-ionospheric coupling effect [25,26,27,28]. A large number of EQ case studies revealed that due to the critical changes in crustal tectonics during the pre-seismic phase of major EQs, the ionospheric electron density (Ne), ion density, and other parameters in the vicinity of the EQ preparation zones may show different degrees of anomalous disturbances [29,30,31,32,33,34,35,36,37,38]. For instance, Liu et al. [29] statistically analyzed EQs with a magnitude of 6.0 or above in Taiwan from 1994 to 1999 and found that the precursors appeared for 1–6 days in the form of an foF2 decline before these EQs. Moreover, the strength and number of precursors suggested that the energy leakage of the Chi-Chi Mw7.7 EQ in 1999 was significant during its preparation period. For a total of 736 EQs worldwide with a M ≥ 6.0 during the period from 2002–2010, Le et al. [32] presented a statistical analysis of the pre-seismic ionospheric anomalies using total electron content (TEC) observations. The results showed that the incidence of the anomalies a few days before the EQs was generally larger than the incidence during the background days. Additionally, the abnormal behavior of the TEC within a few days before the EQs was most likely related to the impending EQs. Similarly, Liu et al. [34] analyzed the MIT TEC data for the Baja California Mw7.2 EQ that occurred on 4 April 2010 and found that the TEC increased near the epicenter throughout the day on March 25, which was potentially associated with this major EQ. The data analysis and model simulations illustrated that an abnormal electric field could form around the impending epicenter and perturb the ionosphere through the LAIC. Furthermore, based on the China Seismo-Electromagnetic Satellite (CSES) data, Li et al. [36] analyzed the ionospheric anomalous disturbances of the Maduo Ms7.4 EQ in 2021 and the Menyuan Ms6.9 EQ in 2022 and revealed that significantly high O+ density and Ne values were observed near the epicenter two weeks before the two EQs. The results of the above studies reconfirmed that ionospheric disturbances can be possibly associated with the occurrence of EQs and may provide a basis for anomaly identification for short-term EQ predictions.
The preparation and occurrence of EQs are complex geophysical process accompanied by material migration, energy release, and information exchange. In the multi-sphere coupling of seismic energy propagation, the vertical span from surface to space ionosphere is large, with variable media and perturbation factors in the intermediate region. The development of remote sensing and ground-based monitoring technologies has promoted studies on the propagation and coupling mechanisms of seismic electromagnetic signals in the lithosphere, atmosphere, and ionosphere. Meanwhile, the comprehensive analysis of multi-sphere coupling anomalies is helpful for understanding the seismogenic processes for major EQs and providing effective anomaly indications for EQ monitoring and forecasting [39,40,41].
In this paper, we investigated the anomalies of the ionosphere, infrared radiation, AEF, and hot spring ions potentially related to the Luding Ms6.8 EQ and comprehensively analyzed the spatial and temporal characteristics of multi-sphere coupling anomalies. Combined with the LAIC mechanism and analysis results, we had a clear understanding of the seismogenic and anomalous coupling process for this EQ, and we provided a reference for the identification of pre-seismic anomalies in the Earth’s multi-sphere structures. Section 2 describes the materials and analysis methods. Section 3 presents our analysis results and descriptions. In Section 4, we analyze and discuss the multi-sphere coupling mechanism associated with the Luding EQ. Finally, the conclusions are provided in Section 5.

2. Materials and Methods

2.1. EQ and Solar–Geomagnetic Data

The Ms6.8 EQ occurred in Luding county, Sichuan province, China at 04:52:18 UT on 5 September 2022. The epicenter was located at 29.59°N and 102.08°E with a depth of ~16 km. The results of the on-site disaster investigation and strong EQ records showed that the intensity of its meizoseismal area was Ⅸ, resulting in 97 deaths and 20 people missing in Luding, Shimian, and other places [42,43].
The seismo-ionospheric coupling effect is an important part of the Earth’s multi-sphere coupling mechanism, while ionospheric disturbances are mainly influenced by the Earth’s magnetic field and solar activities. In order to distinguish whether the ionospheric disturbances were induced by geomagnetic activities, enhanced solar radiation, or seismogenic process for the Luding EQ, we firstly checked and analyzed the changes on the indices—F10.7 (http://www.sepc.ac.cn, accessed on 25 March 2023), Kp (https://www.gfz-potsdam.de, accessed on 25 March 2023), Dst (https://wdc.kugi.kyoto-u.ac.jp, accessed on 25 March 2023), and AE (http://www.sepc.ac.cn, accessed on 25 March 2023). Only the observations under relatively quiet solar–geomagnetic conditions (F10.7 < 120 SFU; Kp < 3; Dst > −30 nT; |AE| < 500 nT) were analyzed in this paper. The changes in each index from 20 August to 10 September 2022 are shown in Figure 1. It can be seen that the relatively quiet solar–geomagnetic conditions were met only on 22–26 August, while the other time periods were accompanied by magnetic storms and substorms. Especially on 4 September, 1 day before the Luding EQ, the geomagnetic perturbations were unusually strong, with the Kp index exceeding six, the lowest Dst index reaching −72 nT, and the AE index reaching close to 1100 nT. Based on the above results, excluding 22 to 26 August, the ionospheric disturbances that appeared in the other time periods should first consider the factors influenced by solar–geomagnetic activities. It should be noted that there were two and three M-class solar flares that occurred on 25 and 26 August, respectively, which may have an impact on the identification of the pre-seismic anomalies.

2.2. TEC Data and Method

In this paper, the TEC data were used to extract the anomalous ionospheric disturbances, including the global ionospheric map (GIM) and the global positioning system (GPS) TECs. We selected the GIM data published by the Center for Orbit Determination in Europe (CODE, http://ftp.aiub.unibe.ch/CODE/, accessed on 25 March 2023) with the spatial resolution of 2.5° × 5° (lat. × lon.). Meanwhile, we solved the GPS TEC time series data from four GPS stations using a GPS TEC inversion algorithm with a sampling rate of 15 min [44]. The distribution of the epicenter and stations are shown in Figure 2, including two AEF stations near the epicenter and four GPS stations from different directions around the epicenter. The TEC data from 20 August to 10 September 2022 were selected as the analysis time period.
In order to analyze the anomalous changes of the GIM and GPS TECs around the epicenter, we extracted the abnormal signals via the sliding interquartile range method with a sliding background of 15 days [29,45]. The method determined a sliding window for every successive 15 days and separately computed the median (M) and the associated interquartile range (IQR) to construct the upper bound M + K × IQR and the lower bound M − K × IQR [33,37,38]. A positive anomaly was situated above the upper bound and a negative anomaly was situated below the lower bound. K represents the threshold coefficient and took the value of two in this paper, considering the spatial environmental interference in the analysis period. The abnormal values were calculated as the following equation.
Δ O b s = { O b s − ( M + 2.0 I Q R )             O b s > M + 2.0 I Q R             0                 M − 2.0 I Q R ≤ O b s ≤ M + 2.0 I Q R O b s − ( M − 2.0 I Q R )             O b s < M − 2.0 I Q R
where Obs, M, and IQR represent the observed value, median value, and interquartile range, respectively (IQR = upper quartile − lower quartile). For the GIM data at 2 h intervals with a spatial resolution of 2.5° × 5° (lat. × lon.), each day corresponded to 62,208 (72 × 72 × 12) measurement points. Based on the above equation, we obtained the daily GIM anomaly distributions by constructing the upper and lower bounds and calculating the corresponding abnormal values on spatial and temporal scales. Similarly, the daily GPS TEC corresponded to 96 measurement points, and we also obtained the temporal variations of the GPS TEC around the epicenter area on a small scale.

2.3. Plasma Data and Method

As the first satellite of the China Geophysical Field Exploration Satellite Program, the CSES was successfully launched on 2 February 2018 and transited in a sun-synchronous orbit with a revisiting period of 5 days. The CSES orbits the Earth approx. 15 times a day, with a descending node at LT14:00 on the dayside and an ascending node at LT02:00 on the nightside [35,36]. Since the ionosphere is more susceptible to solar activities during the dayside than the nightside, the nightside observations were mainly utilized in order to avoid the effects of solar activities [35]. In this paper, the data of the plasma analyzer package (PAP) on board the CSES (http://www.leos.ac.cn, accessed on 25 March 2023) were selected to analyze the characteristics of the anomalous ionospheric disturbances in the vicinity of the epicenter, which mainly detected the parameters, such as the He+ and O+ densities. According to the 5-day revisiting period of the CSES, we selected the satellite single-orbit data on the nightside passing through the study region (20°~40°N, 92°~112°E), divided them by the latitude range at 0.25° intervals, and obtained a total of 81 measurement points each day. Meanwhile, the background data were selected from the first eight revisited orbits of the current orbit and we calculated the percentage deviations of the plasma parameters using the sliding mean method [37,46] and the following equation.
δ O b s = O b s − O b s m e a n O b s m e a n × 100 %
where Obs represents the observed value, including the He+ and O+ densities, while Obsmean represents the average value of the first eight revisited orbits prior to the current observation orbit. The plasma data from 20 August to 10 September 2022 were also selected as the analysis time period.

2.4. MODIS Data and Method

Terra was the first satellite of the U.S. Earth Observation System (EOS). As a polar-orbiting satellite with the primary purpose of observing the Earth’s surface, it was launched on 18 December 1999 and orbits the Earth approximately 16 times a day at an altitude of 705 km. The MODIS (http://modis.gsfc.nasa.gov, accessed on 25 March 2023) is a payload on board the satellite with 36 optical channels, six of which provide detection information related to land surface temperature (LST) [18]. A comparative study of LSTs obtained by field measurements and satellite remote sensing indicated that there was no substantial difference between the results of the dayside and nightside data for the anomaly analysis on a monthly scale [47]. In addition, the environmental disturbances were mostly high-frequency information, and the radiation anomalies associated with major EQs were mostly low-frequency information. Meanwhile, the 21-channel (3.929~3.989 μm) infrared brightness temperature (IBT) data had fewer high-frequency components and a better correlation with the surface temperature, resulting in an easy extraction of the stable background field information [48]. Therefore, we selected the 21-channel IBT data of the MODIS on the dayside to extract the infrared radiation low-frequency information and contrasted and analyzed the monthly-scale anomalies of infrared radiation before and after the Luding EQ.
In this paper, we used the departure method to obtain the IBT anomalies on a monthly scale. The departure was the difference between a value in a series of values and its mean value. The IBT departure of an individual pixel was the difference between the value of a pixel and the average value of that pixel over a period of years. By calculating the IBT departure for all the pixels in the analysis area, we obtained the departure images from space [49]. Based on the above method, we selected the average value of the same monthly data from 2004 to 2022 as the background information, analyzed the IBT anomalies from January to September 2022, and finally obtained the monthly-scale departure images.

2.5. AEF Data and Method

The anomalous disturbances in the regional atmospheric electric field were easily sensed using near-field electrostatic monitoring equipment [21,22]. Based on the cooperation project in 2019 between the Sichuan Earthquake Agency and the National Space Science Center, Chinese Academy of Sciences, 29 new AEF monitoring stations were built near important fracture zones in the Sichuan area. All the stations were equipped with DNDY-3 atmospheric electrostatic monitors with a sampling rate of 1 s for monitoring abnormal changes in the AEF in critical monitoring areas. The rotating electric field instrument used the principle that the conductor in the AEF generates an induced charge to measure the electric field. The electric field sensor consisted of a stator (induction sheet) and a rotor (grounding shield sheet). The rotating rotor caused the stator to be alternately exposed to the electric field or shielded by the grounding shield sheet, resulting in an alternating signal output. Finally, weak signal processing and signal calibration techniques were utilized to ensure an accurate output of the monitoring data. We selected the AEF data from the stations near the epicenter during August–September 2022 for the anomaly analysis without special data processing, and the distribution of the AEF stations is shown in Figure 2. Under fair weather conditions, excluding the influence of meteorological factors such as lightning and rainfall as well as human factors on the intensity of the AEF, the decrease in the AEF gradient in the seismogenic zone could have been an abnormal signal caused by geological tectonic activities [23,24]. We contrasted and analyzed the synchronous anomalies of the AEF stations in the epicenter area, especially the bay-like depression in the AEF intensity.

3. Results and Analysis

3.1. Ionospheric TEC Anomalies

Since solar–geomagnetic activities often lead to global ionospheric disturbances, it was very important to determine the spatial variations of the ionospheric anomalies on a global scale. Based on the GIM data, we generated the spatial distribution of the ionospheric anomalies during the analysis time period. The results showed that the significant ionospheric disturbances were observed around the epicenter on 20, 26, and 30 August and 4 September prior to the Luding EQ. As shown in Figure 3, the daily distribution of the GIM contained 12 small images with intervals of 2 h, represented as UT, and the red star represented the Luding epicenter. Combined with the spatial distribution characteristics, the significant positive ionospheric anomalies which appeared on 4 September in multiple regions around the world were mainly attributed to active solar–geomagnetic conditions (Figure 3d). Moreover, depending on the daily variations of the F10.7, Kp, Dst, and AE indices (Figure 1), the anomalies may have been accompanied by magnetic storms and substorms on 20 and 30 August. Therefore, the positive ionospheric anomalies on 20 and 30 August in the vicinity of the epicenter may have been influenced by solar–geomagnetic activities (Figure 3a–c), and we could not determine the correlation of the ionospheric anomalies with the Luding EQ. However, on 26 August, the negative ionospheric anomalies appeared around the epicenter from 06:00–10:00 UT and were located above the epicenter at 08:00 UT, exceeding −5 TECu (1 TECu = 1016 el/m2) [34] under relatively quiet solar–geomagnetic conditions (Figure 3b). The local and salient characteristics of the ionospheric anomaly distribution were evident on the global scale, which was generally consistent with the seismo-ionospheric anomaly coupling effect.
Due to the lower spatial resolution of the GIM data, we further analyzed the temporal variations of the GPS TEC around the epicenter, using four GPS stations from different directions (Figure 2). Since a single jump point in the observations could affect the judgment of the abnormal signal, the abnormal duration needed to be greater than 2 h in this paper.
The analysis results are shown in Figure 4 alongside the analysis time period from 20 August to 10 September 2022. We found that the multi-station synchronous anomalies were very evident on 20–22, 26, and 30 August and 4 September before the EQ. Among them, there were synchronous positive anomalies on 20–22 and 30 August and 4 September and synchronous negative anomalies on 26 August, which were generally consistent with the analysis results of the GIM. Except for 26 August, all the other synchronous anomalies were primarily associated with active solar–geomagnetic conditions, as described above (Figure 1). Especially on 4 September, the significant positive anomalies appeared above the GPS stations and lasted for a considerable time, affected by strong geomagnetic perturbations. However, on 26 August, synchronous negative anomalies were observed by four GPS stations near the epicenter, with an anomalous amplitude exceeding −4 TECu at 08:00 UT, which further verified the analysis results of the GIM. Both the anomalous distribution of the GIM and the temporal variations of the GPS TEC indicated that the ionospheric TEC anomalies around the epicenter had obvious local and synchronous characteristics, which may have been precursor signals of the Luding Ms6.8 EQ.

3.2. Ionospheric Plasma Anomalies

In order to identify the pre-seismic ionospheric anomalies more accurately, it was necessary to combine multiple observations for the ionospheric multi-parameter anomaly analysis. Therefore, for the comparison between the ionospheric TEC anomalies, we analyzed the ionospheric disturbances observed by the PAP payload on board the CSES. Due to the data missing from 20–22 August, Figure 5 illustrates the variations in the He+ and O+ densities from 23 August to 10 September during the nighttime. The black dotted line represents the latitude position of the Luding epicenter. With a generally consistent trend between the He+ and O+ densities, the ionospheric positive anomalies appeared significantly on 26 and 31 August and 4 September around the epicenter before the EQ. Meanwhile, the local features of the ionospheric anomalies around the epicenter were more obvious on 26 and 31 August. For a more detailed and accurate examination of these positive anomalies, we calculated the percentage deviations in the He+ and O+ densities in the study region (20°~40°N, 92°~112°E) from 20 August to 10 September using the sliding mean method. The results illustrated that both the He+ and O+ densities showed obvious positive anomalies around the epicenter on 26 August under relatively quiet solar–geomagnetic conditions, while remarkable positive anomalies in the other time periods were mostly related to solar–geomagnetic activities, as described above (Figure 6). Compared to the background observations, the percentage deviations in the He+ and O+ densities reached 13% and 19%, respectively, on 26 August in the vicinity of the epicenter with synchronous characteristics. The variations in the He+ and O+ densities on 26 August revealed that there may have been an interfering signal that caused ionospheric plasma anomalies in the localized areas of the epicenter.
Combined with the ionospheric TEC and the plasma analysis results, the ionospheric multi-parameter anomalies all appeared on 26 August close to the epicenter. It should be noted that there were three M-class solar flares that occurred on 26 August. We found that the F10.7, Kp, Dst, and AE indices did not exceed the thresholds, and the M-class solar flares occurred during 11:00–15:00 UT. However, the ionospheric TEC and plasma anomalies were clearly observed close to the epicenter at approx. 08:00 and 18:00 UT, respectively, which were not in the time period of the solar flares. In addition, the local and synchronous characteristics of the ionospheric multi-parameter anomalies were very evident, and did not appear on a large global scale. Therefore, we believed that 26 August exhibited relatively quiet solar–geomagnetic conditions and the observed ionospheric anomalies were likely to be less affected by solar flares. Furthermore, the significant ionospheric anomalies around the epicenter were generally consistent with the seismo-ionospheric coupling effect, which were more likely associated with the Luding Ms6.8 EQ.

3.3. Infrared Radiation Anomalies

Was the crustal tectonic movement accompanied by anomalous changes in terrestrial infrared radiation during the accumulation and release of energy prior to the Luding EQ? To answer this question, we investigated the IBT anomalies from January to September 2022 around the epicenter based on the MODIS data. Figure 7 illustrates the monthly departure images of the remote sensing IBT before and after the Luding EQ. As shown in Figure 7a, large area IBT anomalies were observed in August 2022 in the epicenter and the surrounding region with a maximum abnormal amplitude over 6 K. However, in September 2022, the brightness temperature decreased quickly to below the average value after the EQ (Figure 7b), with an obvious characteristic of short critical changes. In terms of the spatial distribution, the pre-seismic IBT anomalies in August were significantly distributed around the Bayan Har, Qiangtang, Chuandian, and South China blocks with a large anomaly area, which may have been related to the large spatial scale of strong EQ preparation. Meanwhile, the location of the Luding epicenter was at the edge of the IBT anomaly region, exactly at the boundary intersection of the Chuandian and Bayan Har blocks. Therefore, the occurrence of the Luding EQ may have been related to the background of block motion on the Tibetan Plateau and the extrusion deformation between the adjacent blocks.
Combined with the results of the above analysis, the differential motion of the adjacent active blocks and the accumulation and release of stress between the block boundary regions may have been the main factors contributing to this EQ. While the IBT anomalies before the EQ may have reflected the continuous enhancement of the overall stress for the crustal tectonic movement, the anomalies disappeared after the EQ along with an adjustment and weakening of the regional stress. Therefore, the infrared radiation anomalies observed in August 2022 may have been a sign of an upcoming main shock on a large spatial scale.

3.4. AEF Anomalies

As the intermediate region of the Earth’s multi-sphere coupling process, anomalous variations in the AEF were very important for the pre-seismic anomaly analysis. Based on the AEF observations near the epicenter, we analyzed the anomalous variations of the AEF intensity before and after the EQ. The daily variations in the AEF in the region near the epicenter showed that the synchronous AEF anomalies appeared at the Xinglong and Yanzigou stations 23 and 11 km from the epicenter on 25 August and 4 September 2022, respectively (Figure 8). Among them, the anomalies on 25 August appeared from 18:00–19:00 UT with a maximum abnormal amplitude of −1.2 KV/m at the Yanzigou station (Figure 8a), while the anomalies on 4 September appeared from 08:00–09:00 UT with a maximum abnormal amplitude of −0.6 KV/m at the Xinglong station (Figure 8b). In addition, we checked the observations from the Luding meteorological station, which was 17 km from Yanzigou station and 25 km from Xinglong station. The results indicated that the anomalous variations in the AEF on 25 August were possibly influenced by meteorological factors such as rainfall. Therefore, we cannot be sure whether the anomalies on 25 August were precursor signals of the pre-seismic atmospheric electric field. However, on 4 September, excluding the influence of meteorological factors such as lightning and rainfall as well as human factors, the synchronous negative anomalies in the AEF intensity appeared again under fair weather conditions. Due to the strong and persistent geomagnetic perturbations on 4 September, the positive ionospheric anomalies appeared in multiple regions around the world with no remarkable correlation to the Luding EQ. The AEF anomalies were close to the epicenter with a short duration and a feature of bay-like depression, which were likely to be less affected by geomagnetic perturbations on 4 September. Therefore, according to our analysis and judgment, the AEF anomalies 1 day before the upcoming main shock may have been associated with the geological tectonic activities prior to the Luding EQ.

4. Discussion

The 2022 Luding Ms6.8 EQ occurred on the Moxi segment of the Xianshuihe fault with an intensity of IX in the meizoseismal area. As an important active fault on the eastern margin of the Tibetan Plateau in China, 53 EQs of magnitude 5.0 or above had occurred in this fault from 1700 to 2021. However, the Luding Ms6.8 EQ was the most devastating EQ that occurred on the Xianshuihe fault since the Luhuo Ms7.6 EQ in 1973 and the Daofu Ms6.9 EQ in 1981 [43]. The seismogenic tectonic analysis indicated that the seismogenic mechanism was dominated by a left-sided slip and was generally consistent with the interseismic deformation of the Moxi segment in the Xianshuihe fault. The regional dynamical background features revealed that the Luding EQ was the result of a destabilizing rupture in the tectonic context of lateral extrusion on the southeastern margin of the Tibetan Plateau [50].
Seismologists believe that the differential motion between active blocks is a direct factor in the preparation and occurrence of strong EQs, and the boundaries and intersection zones of active blocks are more conducive to the continuous accumulation of regional stress, and thus the occurrence of strong EQs [51,52]. Meanwhile, the thermal infrared observation experiments of rock deformation and rupture showed that there were regular electromagnetic radiation and precursor IBT changes during the elastic deformation and rupture stages of rocks [53,54]. In addtion, the infrared radiation anomalies caused by rock rupture may be a stress-strain infrared radiation effect. The staged radiation enhancement and weakening of the seismogenic zone may respond to the enhancement and weakening of regional stress [55]. For instance, prior to the Wenchuan Ms8.0 EQ in 2008, the Lushan Ms7.0 EQ in 2013, the Jiuzhaigou Ms7.0 EQ in 2017, and the Maduo Ms7.4 EQ in 2021, the staged infrared radiation and ionospheric anomalies were widely confirmed and reported [16,17,18,56,57,58,59,60]. Similarly, the Luding EQ occurred precisely at the boundary intersection of the Chuandian and Bayan Har blocks, and we also identified the multi-sphere coupling anomalies of the ionosphere, infrared radiation, and the AEF in the seismogenic region before the EQ. What was the seismogenic process of the Luding EQ, and how did the subsurface stress changes lead to surface infrared radiation and space ionosphere anomalies?
The LAIC mechanism provides a better explanation. It is believed that the stress enhancement in the seismogenic region will lead to the expansion of old fissures and the creation of new fissures in the underground rocks, and that the noble gases (e.g., radon) in the rocks and soils will then escape. The decay of radon produces high-energy alpha particles, causes air ionization near the ground, and releases large amounts of latent heat, which in turn causes changes in the surface temperature. Meanwhile, radioactive ionizing radiation fills the atmosphere above the ground with an abnormally high number of positive and negative ions, generating a polarized vertical electric field opposite the fair weather conditions, and thus causing AEF anomalies in the seismogenic region. Eventually, the anomalous electric field acts on the ionosphere under certain conditions, leading to anomalous changes in the ionospheric electron concentration and other parameters [5,6,7,12,23]. In this model mechanism, the escape and release of noble gases (e.g., radon) is the key link. The spatial and temporal variations of rock and soil radon gas in the active fault zone can potentially reflect the regional crustal stress changes associated with seismotectonic activities [61,62,63,64]. However, in seismically active areas, can radioactive radiation caused by the release of radon gas lead to anomalies in the atmosphere and ionosphere? Scientists provided different insights into the LAIC mechanism mentioned above. Surkov et al. [65] assessed the relation between the radon emission variations and the vertical atmospheric current flowing into the ionosphere. The theoretical analysis showed that local variations in the background current associated with the release of radon gas in the lower atmosphere had almost no effect on the distribution of electrons in the ionosphere. Only large changes in the overall conductivity of the atmosphere caused perturbations in the ionosphere, which concluded that this hypothesis was not plausible. The implausibility of this hypothesis was also demonstrated by the experimental results of Schekotov et al. [66], who suggested that there was no direct relationship between pre-seismic air ionization by radon and ULF/ELF (1–30 Hz) atmospheric electromagnetic radiation. Nevertheless, the LAIC mechanism based on the release of noble gases (e.g., radon) before major EQs is still recognized by most scholars. For the Luding EQ, due to the small number of precursor monitoring stations in the region near the epicenter, we were unable to validate the radon gas anomalies before the EQ. Fortunately, we identified ion anomalies in the hot springs in the region near the epicenter.
Previous studies found that the distribution of hot springs and their chemical compositions were influenced by the fault structure features, the movement rate, and the circulation depth of groundwater. However, due to rock rupture or crustal stress changes in the seismogenic region, the chemical compositions of hot springs could appear as obvious anomalies before and after EQs. Monitoring the abnormal changes of ion concentrations in hot springs may provide useful insights for seismic precursor recognition [67,68,69,70]. For instance, the ion concentrations in hot springs near the epicenter showed abnormal changes before and after the Wenchuan Ms8.0 and Lushan Ms7.0 EQs. It could be attributed to the water–rock interaction and the input of deep fluids caused by the changes in the crustal tectonic stress [71,72]. After the Luding EQ, the short-term EQ research team from the Institute of Earthquake Forecasting, China Earthquake Administration conducted a seismic scientific expedition in the hot springs near the epicenter (https://www.ief.ac.cn/dzkk/info/2022/69566.html, accessed on 25 March 2023). The survey results of the hot spring ions are as follows. As shown in Table 1, five hot springs within 300 km of the epicenter showed abnormal changes in the ion concentrations, such as Na+, Cl−, and SO42−. The anomalies first appeared in May, concentrated in July and August, and continued until September after the EQ. Among them, the Xinxing hot spring in Luding County, 18 km from the epicenter, showed positive anomalies of SO42− concentrations from 27 May to 28 August 2022. Similarly, the Gongyi sea hot spring in Shimian County, 67 km from the epicenter, showed positive anomalies of Na+ concentrations from 18 May to 24 September 2022 and SO42− concentrations from 20 July to 18 September 2022. The survey results of the hot spring ions anomalies indicated that the seismic hazard of the Xianshuihe fault was significantly enhanced before the Luding EQ. The continuous accumulation of stress in the seismogenic region might have led to the micro-rupture of rocks at the deep seismic source within the fault zone, triggering the disruption of the water–rock interaction equilibrium. The anomalies of ion concentrations in hot springs might have reflected the rock deformation and rupture caused by crustal stress changes in the seismogenic region, which were most likely associated with the seismogenic process of the Luding EQ. In this paper, the observed increases in various ion concentrations in five hot springs were only used as an auxiliary validation of the multi-sphere coupling anomalies. The variations in the ion concentrations and crustal deformation during the longer period prior to the Luding EQ need to be further investigated and analyzed. Meanwhile, the analysis of the hydrochemical anomalies in hot springs associated with the Luding EQ will be presented and discussed in detail in future articles.
On the basis of the analysis results, in order to better display the characteristics of the multi-sphere coupling anomalies before the Luding EQ, the temporal features of the appearance of the anomalies were plotted according to their appearance time, as shown in Figure 9. The results further indicated that the anomalies of the ion concentrations in hot springs appeared notably in July before the EQ and lasted until September after the EQ, followed by infrared radiation anomalies in August and the AEF anomalies on 25 August and 4 September. The ionospheric multi-parameter anomalies synchronously appeared on 26 August. Subsequently, the Ms6.8 EQ occurred in Luding County on 5 September 2022. The characteristics of the multi-sphere coupling anomalies in temporal evolution were obvious. Moreover, the multiple pre-seismic anomalies were strongly correlated with the seismogenic fault, and the anomaly distribution covered the Luding epicenter with obvious local characteristics. Therefore, the multi-sphere coupling anomalies showed a remarkable correlation with the seismogenic process for this EQ.
In summary, we suggest that the continuous accumulation of stress in the seismogenic region led to the micro-rupture of rocks at the deep seismic source, and the water–rock interaction and the input of deep fluids might have caused the ion concentration anomalies in the hot springs. Subsequently, with considerable amounts of rocks being cracked microscopically, the release and decay of radon gas produced high-energy alpha particles, which ionized the surface atmosphere, releasing latent heat and causing the IBT anomalies. Meanwhile, radioactive ionizing radiation generated a significant amount of positive and negative ions. The positive ions formed a strong charge gradient near the ground and generated a polarized vertical electric field opposite to that of a clear day. Then, the AEF anomalies were captured using instruments near the epicenter. Eventually, the anomalous electric field propagated upward under the action of DC electric field coupling and were superimposed into the ionosphere, causing multi-parameter synchronous disturbances in the ionosphere. The analysis results were generally consistent with the LAIC mechanism, which initially revealed the spatio-temporal evolution characteristics of the multi-sphere coupling anomalies prior to the Luding EQ, and provided a reference for the identification of pre-seismic anomalies in the Earth’s multi-sphere structures.
Seismic electromagnetic phenomena are closely related to deep lithospheric tectonics, material motion, atmosphere, and ionospheric media. The anomalous variations in the subsurface fluids, surface infrared radiation, atmospheric electric field, and space ionosphere are important manifestations for the exchange of material and energy in the Earth’s multi-sphere structures during the seismogenic process. For seismic activity monitoring and seismic precursor research, remote sensing and ground-based monitoring technologies play a role in corroborating and compensating each other. The development of multiple monitoring technologies can help us achieve a more comprehensive understanding about the characteristics of multi-sphere coupling anomalies in temporal evolution and spatial distribution, and further explore abnormal coupling correlations and seismic precursor mechanisms. Based on the joint analysis of multi-sphere coupling anomalies, seismic precursors should be traceable with adequate observation methods and observation point layouts. In-depth studies on the energy propagation processes of strong EQs should also be conducted in the future to improve the coupling mechanism of seismic multi-sphere anomalies.

5. Conclusions

In this paper, we investigated the anomalies of the ionosphere, infrared radiation, AEF, and hot spring ions potentially associated with the 2022 Luding Ms6.8 EQ using multi-source data from remote sensing and ground-based monitoring systems. Our conclusions are as follows.
(1)
The ionospheric multi-parameter anomalies were detected on 26 August under relatively quiet solar–geomagnetic conditions and were less affected by solar flares. The observed parameters included the GIM and GPS TECs from ground-based monitoring and the He+ and O+ densities from satellite monitoring. Combined with the spatial and temporal characteristics of the pre-seismic ionospheric disturbances, the stronger seismo-ionospheric coupling effect around the epicenter 10 days before was potentially associated with the Luding EQ. Meanwhile, the joint analysis of the satellite–ground monitoring data was necessary for the judgment of pre-seismic ionospheric anomalies, and effectively increased the confidence of the anomaly recognition.
(2)
In addition to ionospheric disturbances, infrared radiation, the AEF, and hot spring ions all showed pre-seismic anomalous changes. The anomalies in hot spring ions firstly appeared from July to September before and after the EQ, followed by the infrared radiation anomalies in August and the AEF anomalies on 25 August and 4 September. Finally, the ionospheric multi-parameter anomalies appeared on 26 August. The characteristics of the multi-sphere coupling anomalies in temporal evolution and spatial distribution were very obvious, which showed a remarkable correlation with the seismogenic process of the Luding EQ. We suggest that the differential motion and the regional crustal stress accumulation between the Chuandian block and the Bayan Har block might have led to this EQ. The seismic precursor signals propagated upward from the lithosphere to the atmosphere and ionosphere through a geochemical pathway, thus triggering the multi-sphere coupling anomalies, which fully validated the physical mechanism of the LAIC.
(3)
The multi-sphere coupling analysis of the Luding EQ provided a reference for the identification of pre-seismic anomalies in the Earth’s multi-sphere structures and indicated that remote sensing and ground-based monitoring technologies play an important role in corroborating and compensating each other. Based on the joint analysis of the multi-sphere coupling anomalies, seismic precursors should be traceable using adequate observation methods and observation point layouts. Furthermore, it is possible to more accurately predict the location and timing of upcoming main shocks once a clearer understanding of the seismogenic process for major EQs can be obtained.

Author Contributions

Conceptualization, J.L. and X.Z.; methodology, J.L., M.Y. and T.Z.; software, J.L. and X.Z; validation, J.L. and X.Y. (Xianhe Yang); formal analysis, J.L., M.Y. and T.Z.; investigation, J.L., X.Z. and X.Y. (Xianhe Yang); resources, W.W. and G.Q.; data curation, J.L., T.Z., Z.B., X.Y. (Xing Yang) and Q.L.; writing—original draft preparation, J.L.; writing—review and editing, X.Z., X.Y. (Xing Yang) and J.L.; supervision, J.L. and X.Z.; funding acquisition, J.L. and W.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key R&D Program of China (2021YFC3000602-05) and the Earthquake Science and Technology Project of Sichuan Earthquake Agency (LY2220).

Data Availability Statement

CSES data are provided by the website https://www.leos.ac.cn (accessed on 25 March 2023). Terra data are provided by the website http://modis.gsfc.nasa.gov (accessed on 25 March 2023). GIM data are provided by the website http://ftp.aiub.unibe.ch/CODE/ (accessed on 25 March 2023). RINEX data of permanent GPS stations in China are obtained from the Crustal Movement Observation Network of China (CMONOC) and atmospheric data are obtained from National Space Science Center, Chinese Academy of Sciences.

Acknowledgments

We would like to thank the CSES Satellite Center and the Terra Satellite Center for providing the satellite data, and the CODE for providing the GIM data. We are also thankful to the Crustal Movement Observation Network of China for providing the RINEX data and the National Space Science Center, Chinese Academy of Sciences for providing the atmospheric data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kalinin, U.K.; Romanchuk, A.A.; Sergeenko, N.P.; Shubin, V.N. The large-scale isolated disturbances dynamics in the main peak of electronic concentration of ionosphere. J. Atmos. Sol.-Terr. Phys. 2003, 65, 1175–1177. [Google Scholar] [CrossRef]
  2. Sharma, K.; Dabas, R.S.; Sarkar, S.K.; Das, R.M.; Ravindran, S.; Gwal, A.K. Anomalous enhancement of ionospheric F2 layer critical frequency and total electron content over low latitudes before three recent major earthquakes in China. J. Geophys. Res. Space Phys. 2010, 115, A11313. [Google Scholar] [CrossRef]
  3. Oikonomou, C.; Haralambous, H.; Pulinets, S.; Khadka, A.; Paudel, S.R.; Barta, V.; Muslim, B.; Kourtidis, K.; Karagioras, A.; Inyurt, S. Investigation of pre-earthquake ionospheric and atmospheric disturbances for three large earthquakes in Mexico. Geosciences 2021, 11, 16. [Google Scholar] [CrossRef]
  4. Salikhov, N.; Shepetov, A.; Pak, G.; Nurakynov, S.; Ryabov, V.; Saduyev, N.; Sadykov, T.; Zhantayev, Z.; Zhukov, V. Monitoring of Gamma Radiation Prior to Earthquakes in a Study of Lithosphere-Atmosphere-Ionosphere Coupling in Northern Tien Shan. Atmosphere 2022, 13, 1667. [Google Scholar] [CrossRef]
  5. Hayakawa, M. Electromagnetic phenomena associated with earthquakes: A frontier in terrestrial electromagnetic noise environment. Recent Res. Dev. Geophys. 2004, 6, 81–112. [Google Scholar]
  6. Pulinets, S.A.; Ouzounov, D. Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model—An unified concept for earthquake precursors validation. J. Asian Earth Sci. 2011, 41, 371–382. [Google Scholar] [CrossRef]
  7. Molchanov, O.; Fedorov, E.; Schekotov, A.; Gordeev, E.; Chebrov, V.; Surkov, V.; Rozhnoi, A.; Andreevsky, S.; Iudin, D.; Yunga, S.; et al. Lithosphere-atmosphere-ionosphere coupling as governing mechanism for preseismic short-term events in atmosphere and ionosphere. Nat. Hazards Earth Syst. Sci. 2004, 4, 757–767. [Google Scholar] [CrossRef]
  8. Kamogawa, M. Preseismic lithosphere-atmosphere-ionosphere coupling. Eos Trans. Am. Geophys. Union 2006, 87, 417–424. [Google Scholar] [CrossRef]
  9. Kuo, C.L.; Lee, L.C.; Huba, J.D. An improved coupling model for the lithosphere-atmoshere-ionosphere system. J. Geophys. Res. Space Phys. 2014, 119, 3189–3205. [Google Scholar] [CrossRef]
  10. Shahzad, F.; Shah, M.; Riaz, S.; Ghaffar, B.; Ullah, I.; Eldin, S.M. Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 Mw 7.2 Haiti Earthquake. Atmosphere 2023, 14, 347. [Google Scholar] [CrossRef]
  11. Marchetti, D.; De Santis, A.; Shen, X.H.; Campuzano, S.A.; Perrone, L.; Piscini, A.; Di Giovambattista, R.; Jin, S.; Ippolito, A.; Cianchini, G.; et al. Possible Lithosphere-Atmosphere-Ionosphere Coupling effects prior to the 2018 Mw = 7.5 Indonesia earthquake from seismic, atmospheric and ionospheric data. J. Asian Earth Sci. 2020, 188, 104097. [Google Scholar] [CrossRef]
  12. Zhang, X.; Shen, X. The development in seismo-ionospheric coupling mechanism. Prog. Earthq. Sci. 2022, 52, 193–202, (In Chinese with an English Abstract). [Google Scholar]
  13. Barkat, A.; Ali, A.; Rehman, K.; Awais, M.; Riaz, M.S.; Iqbal, T. Thermal IR satellite data application for earthquake research in Pakistan. J. Geodyn. 2018, 116, 13–22. [Google Scholar] [CrossRef]
  14. Ma, W.; Zhang, X.; Jun, L.; Qi, Y.; Bo, Z.; Chong, Y.; Kang, C.; Lu, X. Influences of multiple layers of air temperature differences on tidal forces and tectonic stress before, during and after the Jiujiang earthquake. Remote Sens. Environ. 2018, 210, 159–165. [Google Scholar]
  15. Ouzounov, D.; Liu, D.; Kang, C.; Cervone, G.; Kafatos, M.; Taylor, P. Outgoing long wave radiation variability from IR satellite data prior to major earthquakes. Tectonophysics 2007, 431, 211–220. [Google Scholar] [CrossRef]
  16. Jing, F.; Singh, R.P.; Cui, Y.; Sun, K. Microwave brightness temperature characteristics of three strong earthquakes in Sichuan province, China. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2020, 13, 513–522. [Google Scholar]
  17. Jing, F.; Zhang, L.; Singh, R.P. Pronounced Changes in Thermal Signals Associated with the Madoi (China) M 7.3 Earthquake from Passive Microwave and Infrared Satellite Data. Remote Sens. 2022, 14, 2539. [Google Scholar] [CrossRef]
  18. Zhang, T.B.; Yang, X.; Lu, Q. Variation features of satellite infrared radiation time series associated with Ms ≥ 7.0 earthquakes occurred in the Mid-Eastern Bayan Har block. Chin. J. Geophys. 2023, 66, 1496–1507, (In Chinese with an English Abstract). [Google Scholar]
  19. Smirnov, S. Association of the negative anomalies of the quasistatic electric field in atmosphere with Kamchatka seismicity. Nat. Hazards Earth Syst. Sci. 2008, 8, 745–749. [Google Scholar] [CrossRef]
  20. Mizuno, A.; Takashima, K. Continuous measurement of current in air and possible relation with intense earthquake. J. Electrost. 2013, 71, 529–532. [Google Scholar] [CrossRef]
  21. Choudhury, A.; Guha, A.; De, B.K. A statistical study on precursory effects of earthquakes observed through the atmospheric vertical electric field in northeast India. Ann. Geophys. 2013, 56, R0331. [Google Scholar]
  22. Harrison, R.G.; Nicoll, K.A. Fair weather criteria for atmospheric electricity measurements. J. Atmos. Sol. Terr. Phys. 2018, 179, 239–250. [Google Scholar] [CrossRef]
  23. Chen, T.; Zhang, X.; Zhang, X.; Jin, X.; Wu, H.; Ti, S.; Li, R.; Li, L.; Wang, S. Imminet estimation of earthquake hazard by regional network monitoring the near surface vertical atmospheric electrostatic field. Chin. J. Geophys. 2021, 64, 1145–1154, (In Chinese with an English abstract). [Google Scholar]
  24. Jin, X.B.; Zhang, L.; Bu, J.W.; Qiu, G.L.; Ma, L.; Liu, C.; Li, Y.D. Discussion on anomaly of atmospheric electrostatic field in Wenchuan Ms8.0 earthquake. J. Electrost. 2020, 104, 103423. [Google Scholar] [CrossRef]
  25. Pulinets, S.; Davidenko, D. Ionospheric precursors of earthquakes and Global Electric Circuit. Adv. Space Res. 2014, 53, 709–723. [Google Scholar] [CrossRef]
  26. Ryu, K.; Lee, E.; Chae, J.S.; Parrot, M.; Pulinets, S. Seismo-ionospheric coupling appearing as equatorial electron density enhancements observed via DEMETER electron density measurements. J. Geophys. Res. Space Phys. 2014, 119, 8524–8542. [Google Scholar] [CrossRef]
  27. Klimenko, M.V.; Klimenko, V.V.; Karpov, I.V.; Zakharenkova, I.E. Simulation of Seismo-Ionospheric Effects initiated by internal gravity wave. Russ. J. Phys. Chem. 2011, B5, 393–401. [Google Scholar] [CrossRef]
  28. Le, H.; Liu, J.; Zhao, B.Q.; Liu, L.B. Recent progress in ionospheric earthquake precursor study in China: A brief review. J. Asian Earth Sci. 2015, 114, 420–430. [Google Scholar] [CrossRef]
  29. Liu, J.; Chen, Y.; Pulinets, S.; Tsai, Y.; Chuo, Y. Seismo-ionospheric signatures prior to M ≥ 6.0 Taiwan earthquakes. Geophys. Res. Lett. 2000, 27, 3113–3116. [Google Scholar] [CrossRef]
  30. Liu, J.Y.; Chen, Y.I.; Jhuang, H.K.; Lin, Y.H. Ionospheric foF2 and TEC anomalous days associated with M ≥ 5.0 earthquakes in Taiwan during 1997–1999. Terr. Atm. Ocean Sci. 2004, 15, 371–383. [Google Scholar] [CrossRef]
  31. Liu, J.; Chen, Y.; Chen, C.; Hattori, K. Temporal and spatial precursors in the ionospheric global positioning system (GPS) total electron content observed before the 26 December 2004 M9.3 Sumatra-Andaman Earthquake. J. Geophys. Res. 2010, 115, A09312. [Google Scholar] [CrossRef]
  32. Le, H.; Liu, J.Y.; Liu, L. A statistical analysis of ionospheric anomalies before 736 M 6.0+ earthquakes during 2002–2010. J. Geophys. Res. 2011, 116, A02303. [Google Scholar]
  33. Zhang, X.M.; Wang, Y.L.; Boudjada, M.Y.; Liu, J.; Magnes, W.; Zhou, Y.L.; Du, X.H. Multi-Experiment Observations of Ionospheric Disturbances as Precursory Effects of the Indonesian Ms6.9 Earthquake on August 05, 2018. Remote Sens. 2020, 12, 4050. [Google Scholar] [CrossRef]
  34. Liu, J.; Wang, W.; Zhang, X.; Wang, Z.; Zhou, C. Ionospheric total electron content anomaly possibly associated with the April 4, 2010 Mw7.2 Baja California earthquake. Adv. Space Res. 2022, 69, 2126–2141. [Google Scholar] [CrossRef]
  35. Zhima, Z.; Yan, R.; Lin, J.; Wang, Q.; Yang, Y.; Lv, F.; Huang, J.; Cui, J.; Liu, Q.; Zhao, S.; et al. The Possible Seismo-Ionospheric Perturbations Recorded by the China-Seismo-Electromagnetic Satellite. Remote Sens. 2022, 14, 905. [Google Scholar] [CrossRef]
  36. Li, M.; Wang, H.; Liu, J.; Shen, X. Two Large Earthquakes Registered by the CSES Satellite during Its Earthquake Prediction Practice in China. Atmosphere 2022, 13, 751. [Google Scholar] [CrossRef]
  37. Xie, T.; Chen, B.; Wu, L.; Dai, W.; Kuang, C.; Miao, Z. Detecting seismo-ionospheric anomalies possibly associated with the 2019 Ridgecrest (California) earthquakes by GNSS, CSES, and Swarm observations. J. Geophys. Res. Space Phys. 2021, 126, e2020JA028761. [Google Scholar] [CrossRef]
  38. Dong, Y.; Gao, C.; Long, F.; Yan, Y. Suspected Seismo-Ionospheric Anomalies before Three Major Earthquakes Detected by GIMs and GPS TEC of Permanent Stations. Remote Sens. 2022, 14, 20. [Google Scholar] [CrossRef]
  39. Zhao, S.F. Research of Lithosphere-Atmosphere-Ionosphere Coupling Mechanism Related to Earthquakes. Master’s Thesis, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China, June 2010. [Google Scholar]
  40. Yang, X.B. Study of Lithosphere-Atmosphere-Ionosphere Electric Field Coupling. Ph.D. Thesis, Wuhan University, Wuhan, China, June 2015. [Google Scholar]
  41. Zhong, M.J. A Study on Thermal Infrared Anomalies and Ionospheric Disturbances of Strong Earthquakes. Ph.D. Thesis, Institute of Geology, China Earthquake Administration, Beijing, China, June 2021. [Google Scholar]
  42. Qu, Z.; Zhu, B.J.; Cao, Y.T.; Fu, H.R. Rapid report of seismic damage to buildings in the 2022 M 6.8 Luding earthquake, China. Earthq. Res. Adv. 2023, 3, 100180. [Google Scholar] [CrossRef]
  43. Yi, G.X.; Long, F.; Liang, M.J.; Zhao, M.; Zhang, H.P.; Zhou, R.J.; Li, Y.; Liu, H.; Wu, P.; Wang, S.W.; et al. Seismogenic structure of 5 September 2022 Sichuan Luding Ms6.8 earthquake sequence. Chin. J. Geophys. 2023, 66, 1363–1384, (In Chinese with an English Abstract). [Google Scholar]
  44. Xiong, B. Ionospheric Response to Solar Flare and GPS-TEC Monitoring. Ph.D. Thesis, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China, May 2012. [Google Scholar]
  45. Chen, Y.I.; Chuo, J.Y.; Liu, J.Y.; Puilnets, S.A. Statistical Study of Ionospheric Precursors of Strong Earthquake at Taiwan Area, XXIVth General Ass.; URSI: Paris, France, 1999; p. 745. [Google Scholar]
  46. Wen, Y.Z.; Tao, D.; Wang, G.X.; Zong, J.Y.; Cao, J.B.; Battiston, R.; ZeRen, Z.M.; Shen, X.H. Ionospheric TEC and plasma anomalies possibly associated with the 14 July 2019 Mw 7.2 Indonesia Laiwui earthquake, from analysis of GPS and CSES data. Earth Planet. Phys. 2022, 6, 313–328. [Google Scholar] [CrossRef]
  47. Chen, S.Y.; Liu, P.X.; Liu, L.Q.; Ma, J.; Chen, G.Q.; Hu, X.Y. Comparative analysis between land surface temperatures obtained by field measurement and satellite remote sensing and its implication in earthquake research. Chin. J. Geophys. 2011, 54, 747–755, (In Chinese with an English Abstract). [Google Scholar] [CrossRef]
  48. Liu, F.; Liu, Y.P.; Jiang, L.M.; Xin, H.; Zhang, T.B.; Lu, X. Correlation analysis between Modis brightness temperature and surface temperature provided by meteorological station. Seismol. Geol. 2010, 32, 127–137, (In Chinese with an English Abstract). [Google Scholar]
  49. Hang, T.B.; Lu, Q.; Liu, F.; Xin, H. Analysis on infrared anomalous increase of Modis satellite before Wenchuan M 8.0 and Yushu M 7.1 earthquakes. J. Seismol. Res. 2013, 36, 496–501, (In Chinese with an English Abstract). [Google Scholar]
  50. Shan, X.J.; Li, Y.C.; Gao, Z.Y.; Hua, J.; Huang, X.; Gong, W.Y.; Qu, C.Y.; Zhao, D.Z.; Chen, J.X.; Huang, C.C.; et al. Coseismic deformation of the 2022 Luding Ms6.8 earthquake and seismic potential along adjacent major faults. Chin. Sci. Bull. 2023, 68, 944–953, (In Chinese with an English Abstract). [Google Scholar] [CrossRef]
  51. Zhang, G.M.; Zhang, P.Z. Academic progress on the mechanism and forecast for continental strong earthquake in the first two years. China Basic Sci. 2000, 10, 6–12. [Google Scholar]
  52. Zhang, P.Z.; Deng, Q.D.; Zhang, G.M.; Ma, J.; Gan, W.J.; Min, W.; Mao, F.Y.; Wang, Q. Active tectonic blocks and strong earthquakes in the continent of China. Sci. China Ser. D Earth Sci. 2003, 46 (Suppl. S2), 13–24. [Google Scholar]
  53. Freund, F. Charge generation and propagation in igneous rocks. J. Geodyn. 2002, 33, 543–570. [Google Scholar] [CrossRef]
  54. Wu, L.X.; Liu, S.J.; Wu, Y.H.; Wang, C.Y. Precursors for rock fracturing and failure—Part I: IRR image abnormalities. Int. J. Rock Mech. Min. Sci. 2006, 43, 473–482. [Google Scholar] [CrossRef]
  55. Wu, L.X.; Mao, W.F.; Liu, S.J.; Xu, Z.Y.; Li, Z.W.; Qi, Y.; Miao, C.L. Mechanisms of altering infrared-microwave radiation from stressed rock and key issues on crust stress remote sensing. J. Remote Sens. 2018, 22 (Suppl. S1), 146–161, (In Chinese with an English Abstract). [Google Scholar]
  56. Zhong, M.; Shan, X.; Zhang, X.; Qu, C.; Guo, X.; Jiao, Z. Thermal Infrared and Ionospheric Anomalies of the 2017 Mw 6.5 Jiuzhaigou Earthquake. Remote Sens. 2020, 12, 2843. [Google Scholar] [CrossRef]
  57. Liu, J.Y.; Chen, Y.I.; Chen, C.H.; Liu, C.Y.; Chen, C.Y.; Nishihashi, M.; Li, J.Z.; Xia, Y.Q.; Oyama, K.I.; Hattori, K.; et al. Seis-moionospheric GPS total electron content anomalies observed before the 12 May 2008 Mw7.9 Wenchuan earthquake. J. Geophys. Res. 2009, 114, A04320. [Google Scholar]
  58. Chen, P.; Yao, Y.B.; Chen, J.J.; Yao, W.Q.; Zhu, X.J. Study of the 2013 Lushan M7.0 earthquake coseismic ionospheric dis turbances. Adv. Space Res. 2014, 54, 2194–2199. [Google Scholar] [CrossRef]
  59. Du, X.; Zhang, X. Ionospheric Disturbances Possibly Associated with Yangbi Ms6.4 and Maduo Ms7.4 Earthquakes in China from China Seismo Electromagnetic Satellite. Atmosphere 2022, 13, 438. [Google Scholar] [CrossRef]
  60. Liu, J.; Zhang, X.; Wu, W.; Chen, C.; Wang, M.; Yang, M.; Guo, Y.; Wang, J. The Seismo-Ionospheric Disturbances before the 9 June 2022 Maerkang Ms6.0 Earthquake Swarm. Atmosphere 2022, 13, 1745. [Google Scholar] [CrossRef]
  61. Zhou, X.C.; Du, J.G.; Chen, Z.; Cheng, J.W.; Tang, Y.; Yang, L.M.; Xie, C.; Cui, Y.J.; Liu, L.; Yi, L.; et al. Geochemistry of soil gas in the seismic fault zone produced by the Wenchuan Ms8.0 earthquake, southwestern China. Geochem. Trans. 2010, 11, 5. [Google Scholar] [CrossRef]
  62. Koike, K.; Yoshinaga, T.; Ueyama, T.; Asaue, H. Increased radon-222 in soil gas because of cumulative seismicity at active faults. Earth Planets Space 2014, 66, 57. [Google Scholar] [CrossRef]
  63. Fu, C.C.; Yang, T.F.; Tsai, M.C.; Lee, L.C.; Liu, T.K.; Walia, V.; Chen, C.H.; Chang, W.Y.; Kumar, A.; Lai, T.H. Exploring the relationship between soil degassing and seismic activity by continuous radon monitoring in the Longitudinal Valley of eastern Taiwan. Chem. Geol. 2017, 469, 163–175. [Google Scholar] [CrossRef]
  64. Yang, Y.; Li, Y.; Guan, Z.; Chen, Z.; Zhang, L.; Lv, C.; Sun, F. Correlations between the radon concentrations in soil gas and the activity of the Anninghe and the Zemuhe faults in Sichuan, southwestern of China. Appl. Geochem. 2018, 89, 23–33. [Google Scholar] [CrossRef]
  65. Surkov, V.V.; Pilipenko, V.A.; Silina, A.S. Can Radioactive Emanations in a Seismically Active Region Affect Atmospheric Electricity and the Ionosphere? Izv. Phys. Solid Earth 2022, 58, 297–305. [Google Scholar] [CrossRef]
  66. Schekotov, A.; Hayakawa, M.; Potirakis, S.M. Does air ionization by radon cause low-frequency atmospheric electromagnetic earthquake precursors? Nat. Hazards 2021, 106, 701–714. [Google Scholar] [CrossRef]
  67. Song, S.R.; Chen, Y.L.; Liu, C.M.; Ku, W.Y.; Chen, H.F.; Liu, Y.J.; Kuo, L.W.; Yang, T.F.; Chen, C.H.; Liu, T.K.; et al. Hydrochemical changes in spring waters in Taiwan: Implications for evaluating sites for earthquake precursory monitoring. Terr. Atmos. Ocean. Sci. 2005, 16, 745–762. [Google Scholar] [CrossRef]
  68. Skelton, A.; Claesson, L.L.; Wästeby, N.; Andrén, M.; Stockmann, G.; Sturkell, E.; Mörth, C.M.; Stefansson, A.; Tollefsen, E.; Siegmund, H. Hydrochemical changes before and after earthquakes based on long term measurements of multiple parameters at 2 sites in northern Iceland—A review. J. Geophys. Res. Solid Earth. 2019, 124, 2702–2720. [Google Scholar] [CrossRef]
  69. Nakagawa, K.; Yu, Z.Q.; Berndtsson, R.; Hosono, T. Temporal characteristics of groundwater chemistry affected by the 2016 Kumamoto earthquake using self-organizing maps. J. Hydrol. 2020, 582, 124519. [Google Scholar] [CrossRef]
  70. Wang, B.; Zhou, X.; Zhou, Y.; Yan, Y.; Li, Y.; Ouyang, S.; Liu, F.; Zhong, J. Hydrogeochemistry and Precursory Anomalies in Thermal Springs of Fujian (Southeastern China) Associated with Earthquakes in the Taiwan Strait. Water 2021, 13, 3523. [Google Scholar] [CrossRef]
  71. Chen, Z.; Du, J.; Zhou, X.; Yi, L.; Liu, L.; Xie, C.; Cui, Y.; Li, Y. Hydrochemistry of the hot springs in western Sichuan Province related to the Wenchuan Ms8.0 earthquake. Sci. World J. 2014, 2014, 901432. [Google Scholar]
  72. Chen, Z.; Zhou, X.C.; Du, J.G.; Xie, C.; Liu, L.; Li, Y.; Yi, L.; Liu, H.; Cui, Y.J. Hydrochemical characteristics of thermal spring waters in the Kangding district related to the Lushan Ms = 7.0 earthquake in Sichuan, China. Nat. Hazards Earth Syst. Sci. Discuss. 2015, 2, 1149–1156. [Google Scholar] [CrossRef]
Remotesensing 15 04042 g001 550
Figure 1. Daily variations of F10.7, Kp, Dst, and AE indices from 20 August to 10 September 2022, where (a–d) denote F10.7, Kp, Dst, and AE indices, respectively. In each small image, the black dotted line represents the occurrence time of the Luding Ms6.8 EQ, and the red line represents the threshold value for each index.
Figure 1. Daily variations of F10.7, Kp, Dst, and AE indices from 20 August to 10 September 2022, where (a–d) denote F10.7, Kp, Dst, and AE indices, respectively. In each small image, the black dotted line represents the occurrence time of the Luding Ms6.8 EQ, and the red line represents the threshold value for each index.
Remotesensing 15 04042 g001
Remotesensing 15 04042 g002 550
Figure 2. Distribution of the Luding epicenter and monitoring stations. The red star represents the Luding epicenter, the green triangles and purple circles represent the GPS and AEF stations, respectively. The blue line represents the Moxi segment of the Xianshuihe fault.
Figure 2. Distribution of the Luding epicenter and monitoring stations. The red star represents the Luding epicenter, the green triangles and purple circles represent the GPS and AEF stations, respectively. The blue line represents the Moxi segment of the Xianshuihe fault.
Remotesensing 15 04042 g002
Remotesensing 15 04042 g003a 550Remotesensing 15 04042 g003b 550
Figure 3. Daily distribution of the GIM, where (a–d) denote the daily variations on 20, 26, and 30 August and 4 September 2022, respectively. Each image contains 12 small images with intervals of 2 h, represented as UT. The red star represents the Luding epicenter, and the blue curve represents the magnetic equator.
Figure 3. Daily distribution of the GIM, where (a–d) denote the daily variations on 20, 26, and 30 August and 4 September 2022, respectively. Each image contains 12 small images with intervals of 2 h, represented as UT. The red star represents the Luding epicenter, and the blue curve represents the magnetic equator.
Remotesensing 15 04042 g003aRemotesensing 15 04042 g003b
Remotesensing 15 04042 g004 550
Figure 4. Temporal variations of the GPS TEC from the GPS stations (sctq, scxj, sclt, and scyx) near the epicenter, where (a–d) denote Tianquan, Xiaojin, Litang, and Yuexi stations, respectively. The occurrence time of the EQ is marked with black dotted line, and the grey curve represents the upper and lower bounds. The red curve represents the observed TEC value, and the blue curve represents the abnormal ∆TEC value.
Figure 4. Temporal variations of the GPS TEC from the GPS stations (sctq, scxj, sclt, and scyx) near the epicenter, where (a–d) denote Tianquan, Xiaojin, Litang, and Yuexi stations, respectively. The occurrence time of the EQ is marked with black dotted line, and the grey curve represents the upper and lower bounds. The red curve represents the observed TEC value, and the blue curve represents the abnormal ∆TEC value.
Remotesensing 15 04042 g004
Remotesensing 15 04042 g005 550
Figure 5. Variations in the He+ and O+ densities from 23 August to 10 September. (a) The He+ density; (b) the O+ density. The black dotted line represents the latitude position of the Luding epicenter.
Figure 5. Variations in the He+ and O+ densities from 23 August to 10 September. (a) The He+ density; (b) the O+ density. The black dotted line represents the latitude position of the Luding epicenter.
Remotesensing 15 04042 g005
Remotesensing 15 04042 g006 550
Figure 6. Percentage deviations in the He+ and O+ densities in the study region (20°~40°N, 92°~112°E). (a) The He+ density; (b) the O+ density. The black dotted line represents the latitude position of the Luding epicenter.
Figure 6. Percentage deviations in the He+ and O+ densities in the study region (20°~40°N, 92°~112°E). (a) The He+ density; (b) the O+ density. The black dotted line represents the latitude position of the Luding epicenter.
Remotesensing 15 04042 g006
Remotesensing 15 04042 g007 550
Figure 7. Monthly departure images of the remote sensing IBT, where (a,b) denote August and September 2022, respectively, and the red star represents the Luding epicenter.
Figure 7. Monthly departure images of the remote sensing IBT, where (a,b) denote August and September 2022, respectively, and the red star represents the Luding epicenter.
Remotesensing 15 04042 g007
Remotesensing 15 04042 g008 550
Figure 8. Daily variations in the AEF in the region near the epicenter. (a) The Xinglong and Yanzigou stations on 25 August; (b) the Xinglong and Yanzigou stations on 4 September.
Figure 8. Daily variations in the AEF in the region near the epicenter. (a) The Xinglong and Yanzigou stations on 25 August; (b) the Xinglong and Yanzigou stations on 4 September.
Remotesensing 15 04042 g008
Remotesensing 15 04042 g009 550
Figure 9. Temporal features of the appearance of multi-sphere coupling anomalies. The occurrence time of the Luding EQ is marked with black dotted line.
Figure 9. Temporal features of the appearance of multi-sphere coupling anomalies. The occurrence time of the Luding EQ is marked with black dotted line.
Remotesensing 15 04042 g009
Table
Table 1. The precursory anomalies of ion concentrations in five hot springs before the Luding EQ. (The arrows represent positive anomalies in different time periods).
Table 1. The precursory anomalies of ion concentrations in five hot springs before the Luding EQ. (The arrows represent positive anomalies in different time periods).
No.Hot SpringsLon. (°)Lat. (°)Distance from
Epicenter (km)		Type of ExceptionDuration
1Xinxing102.0629.7518SO42− ↑27 May–28 August 2022
2Gongyi sea102.3929.0267Na+ ↑18 May–24 September 2022
SO42− ↑20 July–18 September 2022
3Guanding101.9629.9542Cl− ↑25 August–24 September 2022
Na+ ↑25 August–24 September 2022
4Chuanxing102.3127.87198Cl− ↑27 July–24 September 2022
Na+ ↑27 July–24 September 2022
5Heba101.5027.10283Na+ ↑2 August–24 September 2022
SO42− ↑2 August–24 September 2022
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, J.; Zhang, X.; Yang, X.; Yang, M.; Zhang, T.; Bao, Z.; Wu, W.; Qiu, G.; Yang, X.; Lu, Q. The Analysis of Lithosphere–Atmosphere–Ionosphere Coupling Associated with the 2022 Luding Ms6.8 Earthquake. Remote Sens. 2023, 15, 4042. https://doi.org/10.3390/rs15164042

AMA Style

Liu J, Zhang X, Yang X, Yang M, Zhang T, Bao Z, Wu W, Qiu G, Yang X, Lu Q. The Analysis of Lithosphere–Atmosphere–Ionosphere Coupling Associated with the 2022 Luding Ms6.8 Earthquake. Remote Sensing. 2023; 15(16):4042. https://doi.org/10.3390/rs15164042

Chicago/Turabian Style

Liu, Jiang, Xuemin Zhang, Xianhe Yang, Muping Yang, Tiebao Zhang, Zhicheng Bao, Weiwei Wu, Guilan Qiu, Xing Yang, and Qian Lu. 2023. "The Analysis of Lithosphere–Atmosphere–Ionosphere Coupling Associated with the 2022 Luding Ms6.8 Earthquake" Remote Sensing 15, no. 16: 4042. https://doi.org/10.3390/rs15164042

APA Style

Liu, J., Zhang, X., Yang, X., Yang, M., Zhang, T., Bao, Z., Wu, W., Qiu, G., Yang, X., & Lu, Q. (2023). The Analysis of Lithosphere–Atmosphere–Ionosphere Coupling Associated with the 2022 Luding Ms6.8 Earthquake. Remote Sensing, 15(16), 4042. https://doi.org/10.3390/rs15164042

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop