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Article

Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate

by
Essam Ghamry
1,
Dedalo Marchetti
2,* and
Mohamed Metwaly
3
1
National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11241, Egypt
2
Istituto Nazionale di Geofisica e Vulcanologia (INGV), 00143 Rome, Italy
3
Archaeology Department, College of Tourism and Archaeology, King Saud University, P.O. Box 2627, Riyadh 12372, Saudi Arabia
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(11), 1318; https://doi.org/10.3390/atmos15111318
Submission received: 30 September 2024 / Revised: 27 October 2024 / Accepted: 31 October 2024 / Published: 2 November 2024
(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)
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Abstract

In this study, we analysed lithospheric, atmospheric, and top-side ionospheric magnetic field data six months before the three earthquake doublets occurred in the last ten years around the Arabian tectonic plate. They occurred in 2014, close to Dehloran (Iran), in 2018, offshore Kilmia (Yemen) and in 2022, close to Bandar-e Lengeh (Iran). For all the cases, we considered the equivalent event in terms of total released energy and mean epicentral coordinates. The lithosphere was investigated by calculating the cumulative Benioff strain with the USGS earthquake catalogue. Several atmospheric parameters (aerosol, SO2, CO, surface air temperature, surface latent heat flux humidity, and dimethyl sulphide) have been monitored using the homogeneous data from the MERRA-2 climatological archive. We used the three-satellite Swarm constellation for magnetic data, analysing the residuals after removing a geomagnetic model. The analysis of the three geo-layers depicted an interesting chain of lithosphere, atmosphere, and ionosphere anomalies, suggesting a geophysical coupling before the Dehloran (Iran) 2014 earthquake. In addition, we identified interesting seismic accelerations that preceded the last 20 days, the Kilmia (Yemen) 2018 and Bandar-e Lengeh (Iran) 2022 earthquake doublets. Other possible interactions between the geolayers have been observed, and this underlines the importance of a multiparametric approach to properly understand a geophysical complex topic as the preparation phase of an earthquake.

1. Introduction

The Saudi Arabian Peninsula corresponds with a tectonic plate with the same name, i.e., the Arabian Plate. The Arabian plate moves globally as a rigid block of rock, as depicted by geodetic studies [1]. Still, more recent and precise research based on several Global Navigation Satellite System (GNSS) stations identified some different motions on a local scale, which can be due to weaker sections of the lithosphere and subsurface magmatic activity [2]. In addition, the whole plate, roughly anti-clockwise, follows a rotation of about half a degree every thousand years [2]. The independent motion of the Arabian Plate began in the late Oligocene and has been reproduced with experiments in the laboratory, which produce different scale tectonic and magmatic processes [3]. The interactions of the Arabian Plate with surrounding plates are complex [4]. They involve an extensional border with the African Plate distributed along the Red Sea and the Gulf of Aden. On the other side, it presents a transcurrent border on the South-East with the Indian Plate, a compressional/transcurrent border on the North and North-East side with the Eurasian plate, and a particular interaction in the North-West with Eurasian and Anatolian plates. At this point, a recent catastrophic earthquake took place on 6 February 2023 at the border between Turkey and Syria [5]. This event was investigated by studying possible signals before its occurrence, such as atmospheric alteration and degassing from active faults, particle precipitations or ionospheric electromagnetic disturbances from Swarm satellites and ionosonde data [6,7]. A possible interplay with the space weather, geomagnetic activity and the Turkey 2023 earthquake was investigated by Ouzounov and Khachikyan [8]. Due to these complex interactions, the seismicity is concentrated on the borders of the Saudi Arabian Peninsula, especially on the Southern and Eastern sides toward Pakistan and Iran, as visible in Figure 1. On the Western side of the Arabian plate, there is evidence of volcanic fields, but the role of magma plumes in the region (e.g., Afar one) is enigmatic [9]. Some models of the asthenosphere and lithosphere settings below and in the surrounding areas have been provided by Sembroni et al. [10], and Stern and Johnson [11] provided information about the composition and depth of the lithosphere of the Arabian Plate.
To search for possible coupling between the lithosphere, atmosphere, and ionosphere, we will focus on the last ten years due to the availability of data from the European Space Agency (ESA) Swarm mission.
In this paper, we will focus on three doublet earthquakes that occurred in the last ten years. Table 1 summaries the main parameters of these selected earthquakes, such as epicentre coordinates, hypocentral depth and magnitude. In order to study the preparation phase, we will consider the doublet as a single event with equivalent magnitude, which is provided by the sum of Richter energy “E” calculated from the magnitude “M” with the following formula:
E J = 10 1.5 × M + 4.8
The above equation is suitable for calculating the total energy released by an earthquake from its magnitude, and preferable, this needs to be the moment magnitude Mw [12]. In the cited book, the sum factor of the exponent is 11.8, but it’s equivalent to 4.8 used in this work because the energy was provided in ergs.
The study of the preparatory phase of the earthquake investigates the possibility of the existence of precursors. This type of study is quite controversial, especially regarding the possibility of predicting a future earthquake [13]. There are anyway two logical steps: one is to identify some pre-earthquake signals, generally at posterior analysis; the second is to use this information in a prospective way. The passage from the first stage to the second one is not trivial, as similar signals induced by earthquakes can be produced from several other natural or anthropogenic sources. The assessment of prediction capability (which is still in the first stage) can rely on a statistical base by Receiving Operating Characteristic (ROC) curves [14], and we tested on previous investigations extended to a large number of seismic events [15,16]. However, the present study does not aim to predict or even assess the possibility of predicting incoming earthquakes. Still, the outcomes may be useful but not sufficient for this ambitious objective.
Possibilities about the existence of electromagnetic precursors can be dated back to 1989, when a magnitude 7.1 earthquake occurred in California (US) close to Loma Prieta on 17 October 1989, and Fraser-Smith et al. [17] identified magnetic anomalies from a ground station close to the earthquake. Similar evidence has also been proposed for other earthquakes that occurred in Japan [18,19], China [20,21,22], and Italy [23,24] by several researchers using different techniques.
Analogous research has been conducted on atmospheric parameters such as variation of surface land temperature before the Mw = 6.2 L’Aquila (Italy) 2009 earthquake [25,26]. The identified anomalies could be the result of underground fluid motion before the earthquake [27]. Some specific techniques have been developed to analyse atmospheric data from satellites, especially the thermal infrared anomalies, such as the Robust Satellite Technique (RST) [28]. The RST has been tested in different areas of the world, including Turkey [29] and Japan [30]. Other studies provide evidence of the link between atmospheric anomalies and seismic events, for example, showing that the intensity of microwave brightness thermal emission is proportional to the magnitude of the incoming earthquake [31]. Other atmospheric parameters, such as aerosol, present signs related to seismic activity [32].
For what concerns the ionosphere, the early studies were concentrated on measurements made from the ground by ionosonde [33,34], and measurements of transmitter/receiver Very Low Frequency (VLF) patches that crossed a seismic active area [35,36]. These types of studies are still ongoing and precious in analysing the possible effects of the preparation of earthquakes in the ionosphere [37,38]. In addition to these investigations, nowadays, several in-situ observations are available, such as Swarm satellites [39] or China Seismo-Electromagnetic Satellite (CSES) [40,41]. Several tools and results have been produced with both satellites in the last few years. For example, Christodoulou et al. [42] and Ouyang et al. [43] developed two tools to extract possible seismo-induced magnetic anomalies from Swarm data. At the same time, De Santis et al. [44] identified a temporal pattern of magnetic anomalies, from Swarm, compatible with the seismic rate of events on the occasion of the M7.8 Nepal 2015 earthquake. Finally, several statistical studies identified that before the earthquakes in the world, the number of anomalies was higher than what is expected from homogeneous random distribution in space and time, even though not all earthquakes are preceded by a significant number of anomalies [15,16,45].
Another category of studies focused on multiple parameters but one earthquake. For example, Wu et al. [46] provided a multiparametric and multi-layer investigation of the above-mentioned L’Aquila 2009 earthquake with a sketch of a possible theoretical explanation of the propagation of the anomalies from one layer to the next involving the cover-sphere. The same framework, but a bit refined and integrated with more data and parameters, was also applied to M7.8 25 April 2015 Nepal earthquake [47], which had already been investigated by Ouzounov et al. [48], which identified a precise chain of lithosphere atmosphere and ionosphere anomaly in the days before the mainshock and even the following event in May 2015 occurred also in Nepal.
Other examples of multiparametric investigations are the one we investigated Mw = 6.7 Lushan (China) 2013, Mw = 7.5 Indonesia 2018 earthquakes or more recent Mw = 7.7 Jamaica 2020 and its close Mw = 7.2 Haiti 2021 earthquakes in the Caribbean area [49,50,51,52]. In all of these events, some interesting patterns have been depicted, suggesting a possible coupling between the lithosphere atmosphere and the ionosphere. However, the parameters involved and the time sequences depicted are different. The reasons for such a difference are not known, but possible explanations could rely on different geological contexts, the magnitude of the event, the focal mechanism, the eventual presence of oceanic water and many more.
Several authors proposed the release of radon as a driver of the pre-earthquake phenomena, and several studies depicted anomalies of radon before the earthquakes, for example, in Italy [53,54] or Nepal [55] or volcano-earthquake-related radon emissions [56]. The release of radon is the basic mechanism of one of the most important models to explain Lithosphere-Atmosphere-Ionosphere Coupling (LAIC), i.e., the one proposed by Pulinets and Ouzounov [57]. They also recently provided some more detailed explanations about the possible mechanisms, especially the chemical and physical reactions that are supposed to happen in the air in a recent book [58]. In particular, they proposed that radon can ionise the air, and this may lead to aerosol particle aggregation; this may bring to the hydration of the formed nucleus of aerosol particles, reducing the water vapour in the air and consequently emitting a large quantity of energy detectable as outgoing longwave radiation [59]. There are also other theories about the explanation of the possible interaction before the earthquake occurrence: for example, Molchan and Hayakawa proposed that the micro-fracturaction due to the increase in the stress on the fault may lead to charge separation, which induced electromagnetic pulsations in the range of Ultra Low Frequency (ULF) waves [60,61]. In addition, the increases in temperature of the soil due to underground changes in preparation for the earthquake may create a vertical pressure gradient in the atmosphere and, consequently, Atmospheric (also known as Acoustic) Gravity Waves (AGW) [62,63]. Finally, Etiope and Martinelli [64] proposed the existence of geo-gases, i.e., they somehow linked the underground motion of fluids with the release of radon on the surface that would be transported by gases such as carbon dioxide or methane.

2. Materials and Methods

In this paper, we applied specific methods to extract anomalous behaviours from the lithosphere, atmosphere, and ionosphere. We selected six months prior to each event and equal for all layers based on our previous investigations, mainly of higher magnitude earthquakes (such as Mw = 7.7 Jamaica 2020 or Mw = 7.2 Haiti 2021 earthquakes [51,52]). For lower magnitude events, as in this study, the anticipation time is supposed to be shorter according to Rikitake’s law [65], also proposed by Scholz [66]. However, using the same period of the previous study provided us an opportunity to directly compare our results. Finally, we compared the three layers to find the possible coupling between the different layers. This approach is rigorous, considering that all the time series presented in the paper are analysed in the same time frame, permitting a direct comparison.

2.1. Data and Methods for Lithosphere

In order to investigate the lithosphere layer, the earthquake catalogue from the United States Geological Survey (USGS) has been retrieved. This global catalogue assures a completeness magnitude (i.e., the smallest earthquake surely detected) of 4.5. However, this can change in the time and place [67]; we checked specifically for each downloaded sub-catalogue. The cumulative Benioff strain has been calculated for each case, according to Mignan [68]. So the released energy of an earthquake “E” is calculated according to Equation (1) and the Cumulative Benioff strain “S” at time “t” with the following equation (from [68]):
S t = ∑ i = 1 i < t E i
The calculation of Cumulative Benioff strain has been reported as a useful tool to estimate seismic acceleration (or better, Accelerated Moment Release—AMR) before large earthquakes in the world [69,70,71]. The seismic activation is the process that starts with some intermediate earthquakes (i.e., small fracture and crack formation), which brings to the characteristic earthquake. The acceleration depicted by the Benioff strain has been shown as an effective method for depicting seismic acceleration [72].

2.2. Data and Methods for Atmosphere

To investigate the status of the atmosphere layer, we retrieved the climatological data from the archive “Modern-Era Retrospective Analysis for Research and Applications”, Version 2 MERRA-2 [73]. A set of parameters have been selected, including Aerosol Optical Thickness (AOT), Sulphur Dioxide (SO2), Carbon Monoxide (CO), surface specific humidity (HUM), Dimethyl Sulphide (DMS), surface air Temperature (T), and Surface Latent Heat Flux (SLHF). The selection of these atmospheric parameters is based on the LAIC theories. For example, Pulinets and Ouzounov [57] predict that air ionisation leads to aerosol particle formations, the release of latent heat (due to the hydration process) and a drop of humidity (as water molecules are attracted due to their polarisation by radon-ionised particles in the air). Other parameters are also selected based on previous studies and shreds of evidence [49,51,74]. For each parameter, the typical historical values have been constructed to search for possible variations in the period before the earthquake. The algorithm MEANS has been utilised for earthquake and volcano eruptions [51]. It’s based on the calculus of the mean and standard deviation for each day estimated on all the available years from 1980 up to 2023. In the historical mean, if one year presents values that are considered an outlier, the whole year has been excluded. This may be due, for example, to volcanic eruptions. Finally, the year of the earthquake is compared with the historical time series, and if one day is overpassed positively, the mean plus two standard deviations is considered anomalous. For humidity, we consider a day anomalous if it’s below the mean minus two standard deviations according to the LAIC theory of Pulinets and Ouzounov, which predicts a drop in humidity [57].
For the temperature, a similar algorithm that produces maps of specific days has been applied to some case studies to compare temperature and magnetic ionospheric anomalies [75]. The algorithm is very similar to MEANS, but it works only on the 30 years previous to the earthquake and pixel-by-pixel. As output, it provides two maps with the deviation value in Kelvin of temperature and its significance in terms of the standard deviation of temperature calculated for that pixel in the previous 30 years.

2.3. Data and Methods for Ionosphere

Specific data from a three-spacecraft Swarm mission has been used, which marks the status of art for monitoring the Earth’s magnetic field from space [76,77]. As of April 17, 2014, Swarm Alpha (SW-A) and Swarm Charlie (SW–C) are located at 460 km altitude. Swarm-Bravo (SW–B) travels in an orbit placed at 510 km altitude with an inclination of 87.8°. In the current study, 1 Hz magnetic field data was used from Swarm satellites, utilising the vector field magnetometer (VFM) data. Original data (selected magnetic satellite product name: MAGX_LR_1B) were provided in local and the North-East-Centre (NEC) coordinate reference frames. The key criteria for anomaly selection are: (1) its occurrence during geomagnetic quiet time to exclude external sources of ionospheric disturbance comes out of it, so the quietest days in the month have been utilised (|Dst| index less or equal than 20 nT and ap index less or equal than 10 nT). (2) The satellite must pass over the location of the epicentre in the Dobrowolsky area between 6:00 p.m. local time and 6 a.m. [44]. Nighttime was chosen to avoid disturbing daily ionospheric activity. Dobrovolsky’s field is considered to be the best approximation of the earthquake preparation field, where it is commonly used by scientists [44,51,78]. Analysis of the Magnetic data from the SWarm (AMSW) algorithm [75] is applied to the Swarm magnetic data. The steps of the AMSW algorithms are as follows: First, we confirm that the data are in the quiet time of the day. Next, we extract the data from the original 1 Hz time resolution common data format (CDF). Next, we calculate the corresponding local time (LT) using (LT = UT + longitude/15). Then, convert geographic latitude to geomagnetic latitude. The VFM data represent the total magnetic field, which consists of crustal and external fields. Consequently, we used the CHAOS-6 model [79] of the Earth’s geomagnetic field to get the core, crustal and external components. To identify more information from the data, the first-time derivative and the cubic spline function are applied to the residual data. The anomalous signals, the object of discussion, were marked with red dashed ovals.

3. Results

Here, the results of the extraction of anomalies from the lithosphere, atmosphere and ionosphere for the three case studies explored have been presented. The study of the six months that preceded the three doublet earthquakes in accordance with our previous studies is presented. These included Mw = 6.7 Lushan (China) 2013 [49], Mw = 7.5 Indonesia 2018 [50], Mw = 7.7 Jamaica 2020 [51] and Mw = 7.2 Haiti 2021 [52] earthquakes. Considering that the equivalent magnitudes of present study earthquakes are between 6.2 and 6.3, the anticipation time of eventual pre-earthquake anomalies is expected to be shorter. A relationship between anticipation time and magnitude of the incoming earthquake was first empirically found by Rikitake [65] and Sholtz [66]. Recently, it has been statistically confirmed that the satellite Swarm magnetic data correlates with global M5.5+ earthquakes following Rikitake’s law [15,16].

3.1. Lithosphere

In order to investigate the ionosphere, the earthquake catalogue from USGS was retrieved (https://earthquake.usgs.gov/earthquakes, last access, 12 September 2024). Each doublet earthquake has been considered as one phenomenon with the mean epicentral coordinates and the equivalent magnitude, and the lithospheric analysis was performed just before the first of the two earthquakes (both excluded). In particular, the following parameters have been considered:
  • M = 6.3, on 18 August 2014 at 32.643° N, 47.700° E
  • M = 6.2 on 15 July 2018 at 13.956° N, 51.727° E
  • M = 6.2 on 1 July 2022 at 26.897° N, 55.280° E
For each earthquake doublet, the seismic events that occurred six months before the doublet earthquake (excluded from the analysis) inside Dobrovolsky’s area [80], defined according to the homonymous radius “rDob”:
r D o b [ k m ] = 10 0.43 · M
The catalogue has been overviewed by ZMap Software Version 7 [81], and in particular, the completeness magnitude for each case study has been estimated by the “maximum curvature” criterion. It was found to be Mc = 4.3 for Dehloran (Iran), 2014, Mc = 4.4 for Kilmia (Yemen), 2018 and Mc = 4.1 for Bandar-e Lengeh (Iran), 2022
Figure 2 shows the results of the computation of the cumulative Benioff strain for the three case studies. It’s worth noting that each case presents a peculiar behaviour. In fact, acceleration can be underlined for Dehloran, Iran, 2014, about three months before the doublet case, and after that, there is an almost linear trend. A linear trend is one that is expected in the absence of partial phenomena and is produced by the basic/background seismicity. A very different case was recorded before the Kilmia, Yemen, 2018 case, as there is a linear trend for the whole period except for the last 15 days before the earthquake doublet, where a sudden increase in the strain is well visible. This can be considered a seismic activation two weeks before the main events. It’s worth mentioning that the same behaviour was present also before the Bandar-e Lengeh, Iran, 2022 earthquake doublet. In fact, in this case, a sudden acceleration in strain release is present from 14 June 2022, i.e., 17 days before the main events. In addition, another clear and sudden acceleration in seismicity was recorded from 15 to 22 March 2022, i.e., about 3.5 months before the main events.
Considering these results, the three earthquake doublets have been preceded by different trends in the cumulative Benioff strain, which means a different accumulation of stress on the fault before the main events. However, some similarities have also been noted. The lithospheric trend will be considered as a base for interpreting the following analyses in the atmosphere and the ionosphere.

3.2. Atmosphere

The climatological data of MERRA-2 was used to investigate the atmosphere, searching for anomalies six months before our three case studies were compared with the multi-year typical values. Surface air temperature, aerosol, SO2, CO, and Dymethil Sulfide (only for sea earthquakes) have been observed. Figure 3 reports the sulphur dioxide time series in the six months before Dehloran (Iran), 2014 earthquake doublet. When the value in the year of the earthquakes (red dashed line) overpassed the yellow bands (i.e., the two standard deviations of historical values), we can classify the specific days as anomalous and further discuss them if eventually related to the incoming seismic event and several anomalous days concentrated from 100 to 80 days before the main events. There are also other spot anomalies, one of which is worth noticing, that occurred just one day before the Dehloran (Iran) events.
In addition, the air surface temperature map of the 7 May 2014 has been shown in Figure 4. This was an anomalous day that preceded 103 days the Dehloran (Iran) earthquake doublet. At the same time, SO2 anomalies were also recorded, as visible from the previous graph in Figure 3. The temperature map is very interesting because the distribution is well-aligned with the plate border. Furthermore, the anomaly that is between 6 K and 8 K more than the typical value is significant, with a level between 2σ and 3σ of the standard variation of the surface air temperature in the 30 years before in the same region (see Figure 4 right).
For the case of Kilmia, Yemen, 2018, we show the aerosol trend in the six months before the event in Figure 5. We selected this parameter because it shows more anomalies in the last 1.5 months before the case study.
Several parameters show anomalies around 25 May 2018. Checking the weather, it seems that the anomalies were caused by a peak of rain in the region that probably induced a release of heat from the ground and other anomalies, but it is worth excluding that they may be linked to the incoming seismic events.
The excluded years could be caused by other natural hazard events. For example, in 1991, the eruption of the Pinatubo volcano in Indonesia emitted an amount of SO2 and aerosol for more than one month in all the regions of the world in the equatorial belt, as shown by Marchetti et al. [82].
Figure 6 presents the time series of SO2 before the Bandar-e Lengeh (Iran) 2022 earthquake doublet. It’s outstanding to underline that the highest and anomalous value of SO2 was recorded on day 171 of the time series, which corresponded to 21 June 2022, well inside the seismic acceleration that preceded the double events that started a few days before. The SO2 could be the product of a degassing caused by the ongoing seismic activity that preceded the main shock.

3.3. Ionosphere

In order to investigate the ionosphere, firstly, all the available tracks of the Swarm satellites that crossed Dobrovoslky’s areas of the three cases have been automatically processed using the code MAgnetic Swarm anomaly detection by Spline analysis (MASS) algorithm [83]. It compared the residual root mean square (rms) in a moving window of 3-degree latitude with the Root Mean Square (RMS) evaluated on the whole track within 50° geomagnetic latitude. If the rms > kt × RMS and geomagnetic conditions were quiet, the windows are defined as anomalous. “kt” is a threshold to define the anomalies and, in this work, has been set equal to 2.5. The selected tracks have been processed using the method described previously in the Data and Methods section. These methods have been applied to the three orthogonal components of the geomagnetic field, X-North, Y-East, and Z-Centre, and then, the residuals are analysed and visualised. In the manuscript, we report an example for each case study in Figure 7, Figure 8 and Figure 9, and all the other tracks are provided in Supplementary Materials.
In particular, Figure 7 shows an interesting track that preceded about 56 days (i.e., roughly two months) of the earthquake doublet of Dehloran (Iran) 2014. The track was acquired by Swarm Charlie on 22 June 2014 at 16:37 UT. The geomagnetic conditions were quiet; in fact, Dst = −8 nT and ap = 3 nT. Also, the Auroral Electrojet (AE) index was quiet for the whole day. The solar wind speed was about 360 km/s, which is a low value below a threshold of 400 km/s, sometimes used to define a higher speed of solar activity (even if there is a change in the function of the solar cycle). The solar flux measured at the wavelength of 10.7 cm was 89 SFU at this time, which also suggested a low solar activity. In addition, we reported in Appendix A the complete plot of solar activity (wind speed and flux) during the whole analysed time for the three case studies. It’s evident that an anomalous signal at 28° N of geomagnetic latitude with an intensity of about 2 nT peak-to-peak is at the same latitude as the future epicentres. Considering that the signal was fully inside Dobrovolsky’s area and acquired in very quiet geomagnetic conditions, we consider it a good candidate for pre-earthquake electromagnetic disturbance. This is similar to the ones identified before the Italian seismic sequence 2016–2017 but with a much higher anticipation time [84].
Figure 8 shows one of the three anomalous days detected by Swarm Alpha and Charlie in the six months before Kilmia, Yemen, 2018, which are 5, 8 February and 15 June 2018. We selected to report in the manuscript the one recorded by Swarm Alpha on 5 February 2018 as it’s the one with more reliable characteristics to be considered as a pre-earthquake-induced signal. In fact, the one acquired on 8 February 2018 shows little other disturbances at several latitudes, suggesting its source could be more a global ionospheric perturbation instead of a local phenomenon, like the one we are searching for. On the other hand, the anomaly detected on 15 June 2018 by Swarm Alpha and Charlie seems too intense (more than 8 nT peak-to-peak) to be possibly induced by an earthquake. The geomagnetic conditions on 5 February 2018 were quiet, as depicted by geomagnetic indexes (Dst = 4 nT, ap = 3 nT). The AE index presents some activity during the day of 5 February 2018, but at the acquisition time and in the two hours before, there was no such particular activity. The solar wind speed was about 400 km/s. The solar flux at 10.7 cm wavelength was 65 SFU, indicating low solar activity. The signal aligned well with Dobrovoslky’s area but was a little more intense on the Arabian plate side. This could be due to different factors: it could be an influence of the land with respect to the sea or the tectonic, which is an extensional context. However, it’s also possible that the source of the anomaly could be something other than the earthquake.
Figure 9 shows an example of an ionospheric magnetic anomalous track before the Bandar-e Lengeh (Iran) 2022 earthquake doublet. It was acquired by Swarm Bravo satellite about 44 days before the seismic events. This track shows anomalous signals in all three components, but the larger one is in the Y-East one. This is expected for internal signals (independently if induced by earthquake or other sources) [85]. It was also identified as the most promising one for searching seismo-induced anomalies [84]. During the magnetic anomaly, the geomagnetic conditions were quiet, as underlined by the geomagnetic indexes (Dst = 4 nT, ap = 7 nT, and AE was low at the time of the track and in the hours before). Solar wind speed was slightly higher than in previous cases and about 440 km/s, but still not at an active level. This could be because the solar cycle phase is closer to the maximum. In fact, the solar flux was also 165 SFU, which is quite normal for this phase of Sun activity. Furthermore, the anomaly is well within Dobrovoslky’s area and crosses the whole region of plate interaction involving the Gulf of Aden.

4. Discussion

This paper extracted anomalies from the lithosphere, atmosphere, and ionosphere during the six months before three earthquake doublets that involved the Arabian plate from 2014 to 2022. Figure 10 summarises all of these results. It reports the cumulative Benioff strain for lithosphere analysis and the physical and chemical anomalies from the climatological dataset MERRA-2 for the atmosphere and Swarm magnetic anomalies for the ionosphere. Only the parameters that show one or more anomalies are visualised.
Notably, for the Dehloran (2014) Iran earthquake doublet, the seismic trend from −140 to −88 days is very similar to the atmospheric anomalies from −140 to −68 days, suggesting a coupling between the geo-layers. Looking better at the ionospheric trend, it’s possible to see a sharp increase in anomalies from −63 to −41 days. This is very well compatible with a model of lithosphere atmosphere ionosphere coupling with a slow process that takes several days or even weeks to propagate from one layer to the next. In fact, we can roughly estimate a delay of 10 days to transmit the anomaly from one layer to the upper one. It’s worth noting that this time is well compatible with the one necessary for geo-gases to transport from hypocentral depth to the ground, according to Etiope and Martinelli [64]. Considering they could transport radon in the atmosphere, it takes a few days (half-life of 3.8 days) to decay, emitting radiation (alpha and beta radiation) that can ionise the area up to changing the atmospheric and consequently ionospheric electric circuit [86,87]. This process can take some days, and our chain of anomalies can be potentially explained by such theories. The involved atmospheric parameters for the Dehloran (2014) Iran earthquake doublet are the SO2 and CO after the sharp increase of lithospheres cumulative Benioff strain and later increase of aerosols. It could be explained by the Pulinets and Ouzounov model [57], which predicts the formation of aerosol particles in the atmosphere. A similar release of SO2 before a different and stronger earthquake, Mw = 7.7 Jamaica 2020 earthquake [51], was reported.
The Kilmia, Yemen 2018 case study is less “perfect” than the previous one. However, two interesting possible couplings have been noticed. In fact, the two increases in atmospheric anomaly trends occurred just before the only two times (about −160 and −30 days) the ionosphere showed some anomalies by Swarm satellites. The first possible coupling seems synchronous between the atmosphere and ionosphere, while the second one seems to present a delay of more than ten days. If they are couplings between the geo-layers, then they must be produced by a different mechanism: one fast, such as ULF electromagnetic waves [31,32] and the second one, a slow chain of processes to propagate the anomaly from the atmosphere to the ionosphere [57,88], as we noticed for the previous case. A similar long delay time for the large M7.5 Indonesia 2018 earthquake [20] has been reported, and a combination of more coupling mechanisms investigating the preparation of the Mw = 6.7 Lushan (China) 2013 earthquake [49] was identified. Finally, the seismic trend shows a clear acceleration ten days before the Kilmia (Yemen) 2018 earthquake doublet. It was followed by an increase in anomalies in the atmosphere, which the increase in lithospheric stress may have induced. Still, we missed eventual ionospheric anomalies related to this final process. This may be due to a lack of observations in geomagnetic quiet conditions, or our method is not sufficiently sensible, or the chain of anomalies stopped at the atmosphere for electromagnetic conditions that inhibited the electrical coupling with the ionosphere.
In the case of Bandar-e Lengeh, Iran, 2022, a possible coupling between lithosphere and atmosphere at about −110 days is worth mentioning. In fact, the atmosphere recorded an increase in temperature and later in surface latent heat flux, which is a parameter known in the literature as prone to earthquake preparation [89,90]. Regarding the ionosphere, the trend is not so peculiar, but it’s possible to note a higher rate of anomalies in the last two months before the earthquake.
It’s worth noting that the three investigated cases are all doublet earthquakes with a similar magnitude (equivalent to 6.2 or 6.3). They occurred at the border of the same tectonic plate, the Arabian one, interacting with different plates: the Indian for Iran in 2014 and 2022 cases and the African plate in the case of Yemen in 2018. The different involved plate boundaries are in agreement with the focal mechanism of the single earthquakes: reverse for the Arabian-Indian plate boundary and strike-slip for the Arabian-Africa plate boundary. The latter boundary can also produce normal fault earthquakes for the rift formation or strike-slip due to plate rotation. Regarding the location, the Dehloran (Iran) 2014 case occurred in the land contest (i.e., continental earthquake); the Kilmia (Yemen) 2018 occurred in the ocean, and the Bandar-e Lengeh (Iran) 2022 in the land but close to the sea. In fact, the preparation area for the last case is partially in the sea. The presence of oceanic water can play a role in loading above the lithosphere, and this could explain the analogies between the latter two cases for the seismic acceleration less than 20 days before their occurrences and other analogies. On the other hand, the acceleration 3 months before both Iran cases (of 2014 and 2022) could be explainable with a characteristic of this specific plate boundary. It’s important to mention that even a similar area from a deeper seismological study could reveal different backgrounds and clusters of seismicity as identified in the northeast region (close to Tehran) investigated by Talebi et al. [91]. Nevertheless, these three cases can be considered a starting point for an objective comparison, but many more cases are necessary to draw some deeper conclusions.

5. Conclusions

In this paper, we have analysed the six months before three doublet earthquakes occurred between 2014 and 2022 around the Arabian Plate in order to search for possible Lithosphere Atmosphere Ionosphere Coupling (LAIC) pieces of evidence before their occurrences. For this purpose, several homogeneous data have been analysed (USGS earthquake catalogue, atmospheric parameters from MERRA-2 and Swarm satellite magnetic data) before all three cases object of this work. This provided a unique occasion to compare the cases using the same data, methodologies and period under study. It is possible to conclude:
  • A possible lithosphere atmosphere ionosphere coupling three months before the Dehloran (Iran) 2014 earthquake doublet with a delay time of a few days between one geo-layer and the upper one.
  • Two possible atmosphere-ionosphere couplings are depicted before the Kilmia (Yemen) 2018 earthquake with different coupling mechanisms due to the higher delay of the second one.
  • A very interesting seismic acceleration preceded the last 20 days the occurrence of both Kilmia (Yemen) 2018 and Bandar-e Lengeh (Iran) 2022. In both cases, the atmosphere seems to show a response with an increase in anomaly, suggesting a possible lithosphere-atmosphere coupling.
Finally, these investigations show that there are good candidates as possible chains of anomalies in the lithosphere atmosphere and ionosphere before earthquake occurrence. Further research urges us to identify the reasons for the different patterns. For example, the compressional side of the Arabian plate could explain the seismic acceleration identified for the two earthquake doublets in Iran in 2014 and 2022. Still, it’s unclear, while only the second one shows a seismic acceleration in the last days before the first shock. Other case studies and other data from remote sensing, as well as open platforms to collect and process data, such as the EPOS Data Portal [92], could help better understand the complicated processes in the lithosphere and, hopefully, their impact on the atmosphere and ionosphere. Even though earthquake prediction is not the scope of this paper, and we think that knowledge is not sufficient, these findings contribute valuable insights that could be foundational in the development of a future system aiming at earthquake prediction.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos15111318/s1. The other atmospheric time series, maps of anomalous temperature and the Swarm anomalous tracks before the three case studies.

Author Contributions

Conceptualisation, methodology, software, formal analysis, investigation, data curation, visualisation, writing—original draft preparation D.M. and E.G.; validation, resources, project administration, funding acquisition M.M.; writing—review and editing, D.M., E.G. and M.M.; supervision, D.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Research Supporting Project number (RSP2024R89), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Earthquake catalogue is freely provided by USGS at web-address: https://earthquake.usgs.gov/earthquakes (last accessed 21 September 2024). MERRA-2 data can be downloaded from https://disc.gsfc.nasa.gov/datasets?project=MERRA-2 (last accessed 21 September 2024) with Earth Observation NASA free credential. Swarm data are freely available via ftp and http at swarm-diss.eo.esa.int server (last accessed 21 September 2024). Solar radio flux has been retrieved from https://www.spaceweather.gc.ca/forecast-prevision/solar-solaire/solarflux/sx-5-en.php (last accessed 22 October 2024).

Acknowledgments

Some of the used algorithms in this paper were developed together with other research that we acknowledged; They are Alessandro Piscini, Francisco Javier Pavón-Carrasco, Saioa Arquero Campuzano, Angelo De Santis and Maurizio Soldani. The authors acknowledge ISSI and ISSI-BJ for supporting the International Team 553 “CSES and Swarm Investigation of the Generation Mechanisms of Low Latitude Pi2 Waves” led by Essam Ghamry and Zeren Zhima, and International Team 23-583 (57) “Investigation of the Lithosphere Atmosphere Ionosphere Coupling (LAIC) Mechanism before the Natural Hazards” led by Dedalo Marchetti and Essam Ghamry. The authors would like to thank the Research Supporting Project number (RSP2024R89), King Saud University, Riyadh, Saudi Arabia for funding this work.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A. Space Weather Conditions

This appendix shows the space weather conditions as monitored by the Advanced Composition Explorer (ACE) satellite placed in Lagrangian point L1 facing the Sun and about 1.5 million km away from the Earth. Figure A1 shows the solar wind speed measured in the investigated period before the three case studies analysed in this paper. The data during last days before the Bandar-e Lengeh (Iran) 2022 earthquake are not available due to satellite issue. In analogy, Figure A2 represents the Solar radio flux at the wavelength of 10.7 cm in the Solar Flux Unit (SFU) as normalised for Earth-Sun distance and applied a corrected factor of 0.9 according to URSI (Union Radio-Scientifique Internationale) standard.
Atmosphere 15 01318 g0a1
Figure A1. Solar wind speed during the analysed period before the three doublet earthquakes.
Figure A1. Solar wind speed during the analysed period before the three doublet earthquakes.
Atmosphere 15 01318 g0a1
Atmosphere 15 01318 g0a2
Figure A2. Solar radio flux F10.7 during the analysed period before the three doublet earthquakes.
Figure A2. Solar radio flux F10.7 during the analysed period before the three doublet earthquakes.
Atmosphere 15 01318 g0a2

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Figure 1. Historical seismicity of Arabian Plate. The map shows the M6.0+ earthquakes recorded by USGS from 1 January 1900 up to the present time (i.e., 15 August 2024). The investigated earthquakes are marked with red stars.
Figure 1. Historical seismicity of Arabian Plate. The map shows the M6.0+ earthquakes recorded by USGS from 1 January 1900 up to the present time (i.e., 15 August 2024). The investigated earthquakes are marked with red stars.
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Figure 2. Cumulative Benioff Strain calculated the three case studies of earthquakes from USGS inside Dobrovoslky’s area that occurred six months before the doublet. The blue dashed vertical line represents the first earthquake of each doublet case, and it’s excluded from the red curve.
Figure 2. Cumulative Benioff Strain calculated the three case studies of earthquakes from USGS inside Dobrovoslky’s area that occurred six months before the doublet. The blue dashed vertical line represents the first earthquake of each doublet case, and it’s excluded from the red curve.
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Figure 3. Time series of sulphur dioxide six months before Dehloran (Iran), 2014 earthquake doublet. The blue line represents the typical trend of SO2 calculated on the other years (2014 is excluded) from 1980 to 2023. Its standard deviation is represented by light blue, green, and yellow bands that stand for 1.0, 1.5, and 2.0 times the standard deviation. The red dashed line is instead the SO2 value observed in the year of the earthquake doublet, i.e., 2014.
Figure 3. Time series of sulphur dioxide six months before Dehloran (Iran), 2014 earthquake doublet. The blue line represents the typical trend of SO2 calculated on the other years (2014 is excluded) from 1980 to 2023. Its standard deviation is represented by light blue, green, and yellow bands that stand for 1.0, 1.5, and 2.0 times the standard deviation. The red dashed line is instead the SO2 value observed in the year of the earthquake doublet, i.e., 2014.
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Figure 4. Map of Surface Air Temperature on 7 May 2014, an anomalous day from the corresponding time series. The left map shows the difference in temperature (ΔT) pixel-by-pixel with respect to the average temperature in the previous 30 years; right side: the map shows the statistical significance of the difference in terms of standard deviations of the temperature in the previous 30 years for each specific pixel. The white star represents the mean epicentre of the doublet earthquake, and the yellow circle is Dobrovolsky’s area.
Figure 4. Map of Surface Air Temperature on 7 May 2014, an anomalous day from the corresponding time series. The left map shows the difference in temperature (ΔT) pixel-by-pixel with respect to the average temperature in the previous 30 years; right side: the map shows the statistical significance of the difference in terms of standard deviations of the temperature in the previous 30 years for each specific pixel. The white star represents the mean epicentre of the doublet earthquake, and the yellow circle is Dobrovolsky’s area.
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Figure 5. Time series of aerosol six months before Kilmia, Yemen, 2018 earthquake doublet. The meaning of the lines and colour is the same as in Figure 3. The following years have been automatically excluded from the statistics for outliers: 1991, 2003, 2008, 2009, 2012, 2013, 2015, 2017, and 2020.
Figure 5. Time series of aerosol six months before Kilmia, Yemen, 2018 earthquake doublet. The meaning of the lines and colour is the same as in Figure 3. The following years have been automatically excluded from the statistics for outliers: 1991, 2003, 2008, 2009, 2012, 2013, 2015, 2017, and 2020.
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Figure 6. Time series of sulphur dioxide six months before Bandar-e Lengeh (Iran), Iran, 2022 earthquake doublet. The meaning of the lines and colour is the same as in Figure 3. The following years have been automatically excluded from the statistics for outliers: 1991, 2001.
Figure 6. Time series of sulphur dioxide six months before Bandar-e Lengeh (Iran), Iran, 2022 earthquake doublet. The meaning of the lines and colour is the same as in Figure 3. The following years have been automatically excluded from the statistics for outliers: 1991, 2001.
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Figure 7. Residual of Swarm Alpha (above) and Charlie (below) magnetic data acquired on 22 June 2014 at 16:37 UT. The green circle represents Dobrovolsky’s area of the Dehloran (Iran) 2014 earthquake doublet. The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake.
Figure 7. Residual of Swarm Alpha (above) and Charlie (below) magnetic data acquired on 22 June 2014 at 16:37 UT. The green circle represents Dobrovolsky’s area of the Dehloran (Iran) 2014 earthquake doublet. The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake.
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Figure 8. Residual of Swarm Alpha (above) and Charlie (below) magnetic data acquired on 5 February 2018 at 17:15 UT. The green circle represents Dobrovolsky’s area of the Kilmia, Yemen, 2018 earthquake doublet. The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake.
Figure 8. Residual of Swarm Alpha (above) and Charlie (below) magnetic data acquired on 5 February 2018 at 17:15 UT. The green circle represents Dobrovolsky’s area of the Kilmia, Yemen, 2018 earthquake doublet. The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake.
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Figure 9. Residual of Swarm Bravo magnetic data acquired on 18 May 2022 at 21:07 UT. The green circle represents Dobrovolsky’s area of the Bandar-e Lengeh, Iran, 2022 earthquake doublet. The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake.
Figure 9. Residual of Swarm Bravo magnetic data acquired on 18 May 2022 at 21:07 UT. The green circle represents Dobrovolsky’s area of the Bandar-e Lengeh, Iran, 2022 earthquake doublet. The red vertical line is the projection of the satellite orbit on the map. The mean epicentre is shown as a green asterisk. The red dashed ellipse marks the anomaly potentially associated with the earthquake.
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Figure 10. Summary of lithospheric, atmospheric and ionospheric investigations in the six months before the three cases under study.
Figure 10. Summary of lithospheric, atmospheric and ionospheric investigations in the six months before the three cases under study.
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Table 1. List of the selected earthquakes for this study.
Table 1. List of the selected earthquakes for this study.
Closest TownOrigin Time [UT]Epicentral CoordinatesHypocentral DepthFocal MechanismMagnitudeEquivalent Magnitude of the DoubletSpatial DistanceTemporal Difference
LatitudeLongitude
Dehloran (Iran)18 August 2014 02:32:0532.703° N47.695° E10.2 kmreverse6.26.314 km16 h 40 m 27 s
18 August 2014 18:08:2232.583° N47.704° E5.0 kmReverse/oblique6.0
Kilmia, Yemen15 July 2018 01:57:1914.063° N51.737° E10.0 kmStrike-slip6.06.224 km11 h 11 m 57 s
15 July 2018 13:09:1613.848° N51.717° E10.0 kmStrike-slip6.0
Bandar-e Lengeh (Iran)1 July 2022 21:32:0726.906° N55.239° E16.0 kmreverse6.06.211 km1 h 53 m 6 s
1 July 2022 23:25:1326.888° N55.321° E9.0 kmreverse6.0
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Ghamry, E.; Marchetti, D.; Metwaly, M. Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate. Atmosphere 2024, 15, 1318. https://doi.org/10.3390/atmos15111318

AMA Style

Ghamry E, Marchetti D, Metwaly M. Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate. Atmosphere. 2024; 15(11):1318. https://doi.org/10.3390/atmos15111318

Chicago/Turabian Style

Ghamry, Essam, Dedalo Marchetti, and Mohamed Metwaly. 2024. "Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate" Atmosphere 15, no. 11: 1318. https://doi.org/10.3390/atmos15111318

APA Style

Ghamry, E., Marchetti, D., & Metwaly, M. (2024). Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate. Atmosphere, 15(11), 1318. https://doi.org/10.3390/atmos15111318

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