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Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt

  • Hamada Saadalla EMAIL logo , Saleh Qaysi , Takumi Hayashida and Mona Hamada
Published/Copyright: May 2, 2025
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Open Geosciences
From the journal Open Geosciences Volume 17 Issue 1

Abstract

Aswan broadband seismic network with highly sensitive sensors and good station coverage gave the opportunity to study the seismicity distribution, focal depth, the fault plane solution, the attenuation of seismic wave, the station sites response, and the source spectra of Aswan earthquakes with magnitude ( M L ) between 0.8 and 4.2 recorded from 2010 to 2023 comprehensively. Preliminary analysis of Aswan seismicity during the studied period indicated strike-slip mechanism with minor normal sense is dominant, relatively deep seismicity concentrated beneath Gabal Marawa, whereas shallow seismicity are dominant features of other zones in Aswan region, and the epicenter distribution characterized by cluster forms, frequently occurred earthquakes in the same patches, and concentrated in the intersection area between the two orthogonal fault systems. A generalized inversion technique (GIT) constrained by reference site is applied to separate the path effect, the recording station sites responses and the source spectra from the observed P- and S-waves by means of iterative least square analysis. The separated station sites effects show similar trend using P- and S-waves, with flat curve in the low frequency band from 0.8 to 8 Hz, whereas the station sites responses have peak amplitudes deviated from 4 to 10 in the higher frequencies. The attenuation through propagation is evaluated and empirically formulated Q p = ( 133 ± 2.09 ) f ( 0.54 ± 0.034 ) and Q s = ( 91 ± 1.9 ) f ( 0.8 ± 0.045 ) for P- and S-waves, respectively. The given attenuation fitting relation for P- and S-waves indicated the frequency dependence of seismic wave’s attenuation in the study area. The low-quality factor Q 0 at reference frequency ( f 0 ) pointed that Aswan region is an active region. Furthermore, the low value of Q 0 would indicate that the medium is complex and highly heterogeneous. The third element separated from the observed seismogram is the displacement source spectra which modulated using Brune’s omega square. The advanced earthquake’s source parameters (seismic moment, corner frequency, moment magnitude, and static stress drop and source radius) and its scaling relations are computed using the converted windows of P- and S-waves. The displacement source spectra for seismic events with M L < 3 decayed rapidly at 20 Hz in the frequency bands of 0.8–50 Hz, whereas it is decreased rapidly at 10 Hz for seismic events with magnitude 3 ≤ M L < 4.2 . The seismic moment, the source radius, and the corner frequency range from 9.50 × 1016 to 2.18 × 1021 dyne-cm, from 28 to 190 m, and from 4 to 36 HZ, respectively. The observed stress drops for the studied earthquakes vary from 0.01 to 12 Mpa, whereas the stress drop for S-portion ∆ σ S ranged from 0.03 to 22 Mpa. The low stress drop values may reflect that reservoir–triggered earthquakes may have a lower stress drop than tectonic and crustal ones.

Keywords: Aswan seismicity; P- and S-waves spectral inversion; attenuation; and site characterization

1 Introduction

On 14 November, 1981, a moderate earthquake with magnitude ( M L 5.6) struck the western part of Aswan in Kalabsha village about 55 km from Aswan High Dam at latitude 23.55 N and longitude 32.55 E, and depth of 21 km (along Kalabsha fault zone). According to Kebeasy et al. [1], this earthquake originated from oblique fault (strike slip with normal components). The quake was felt in various locations around the epicenter. The 14 November, 1981 earthquake is the largest instrumental earthquake that occurred in Aswan till date. This moderate earthquake in Aswan region addressed the necessities to study the nature and characteristic of seismicity that nucleated in Aswan region, southern Egypt. The ground motions attenuation equations, site characterization, and the advanced source parameters scaling relations, constitutes the main ingredients for ground motion simulation and prediction equations. Therefore, it is very important to make a comprehensive analysis of the path, site, and source characteristic for seismic event occurring in Aswan region. Many researchers investigated the earthquakes source parameters in the past and recent years for few micro-earthquakes (less than 20 seismic events) that occurred in the vicinity of Aswan. However, they neglected the correction of the effects of the station sites and attenuation in their analysis [2,3,4]. Good station coverage and continuous recording of seismicity in Aswan region resulted in a comprehensive study of Aswan seismic source nature and characteristics by many Egyptian and foreign seismologists. Seismicity distribution studies reveal that Aswan seismicity concentrated in two distinct earthquake clusters, the first cluster is at the intersected area between the two orthogonal fault systems, and can be divided into separate seismic zones [5,6,7,8]. Focal depth distribution studies concluded that Aswan seismicity may be classified into two separated focal depths. The deep seismicity occurred beneath Gabal Marawa with focal depth range from 12 to 30 km, whereas all other seismic activities in the study region are shallower than 12 km. The shallow earthquake group at a depth of less than 12 km is characterized by a swarm-like activity, frequently occurred, and repeated in the same fault patches, whereas the deeper seismicity is characterized by typical foreshock, mainshock–aftershock sequences, and vanishes with time [9]. Focal mechanism studies indicated that the fault movement in Aswan region dominated by strike slip faulting with normal slip sense [6,8,10]. Many researchers suggested that the seismicity in the investigated region is driven by the pore pressure due to the impoundment of water in the Lake reservoir, and concluded that the main factors triggering the seismicity in Aswan region are the preexisting faults trends, the water load, and the time necessary for the water to move into or out of the pore space [9,11]. Preliminary analysis of Aswan seismicity distribution, focal depth classification, and fault plane solution are investigated during this study. The current work utilizes over 1,200 earthquakes covering a broader magnitude range and incorporates both P- and S-wave spectra for inversion. This allows for a more detailed investigation of attenuation and site response at local sites in the Aswan region. Additionally, we compare the dynamic source parameters and scaling relationships of P- and S-wave processes, providing essential input for seismic hazard assessment in the study area. A linear spectral inversion of P- and S- waves was applied to separate the contributions of path attenuation and station sites effects from the observed velocity seismogram by linear least square analysis. The corrected earthquake sources spectra of P- and S-wave are used to determine the advanced source parameters. In addition, the site responses of all stations that recorded the seismic events used in the inversion processes are evaluated. Finally, the attenuation equations that control the propagation of body waves in the study region are deeply studied.

1.1 Geology and structure setting of Aswan region

Aswan region, south Egypt (Figure 1a) is characterized by low rainfall, low erosion and deposition rates, virtual absence of vegetation, and is an arid region. In such ideal environment, the geological, geomorphological, stratigraphical, and structural evidences are preserved for millions of years [12]. The area is relatively flat, with Gabal Marawa and Sinn el Kaddap scarp are the dominant topographic features of the study area. From east to west, four major geomorphological features are present in Aswan region. The Aswan hills and Nile River valley are in the east, while the Nubian plain and Sinn el Kaddap plateau are in the west (Figure 1b). Rocks that are exposed in Aswan region are composed of igneous and metamorphic Precambrian units, that are unconformably overlain by a series of sedimentary layers deposited from late cretaceous to Eocene. From oldest to youngest are the Nubian, Dakhla, Garra, and Dungul formations. The motion of late Cenzoic resulted in the creation of the two orthogonal fault systems in the southern part of Egypt. The N-S faults (Khor el Ramla-Kurkur, Abu Dirwa, Gazelle faults) intersected with the E-W faults (Kalabsha and Seiyal) trend (Figure 1b). Slip tendency studies by Woodward Clyde Consultants [12] concluded that the E-W striking faults had longer total displacement and with greater seismic activity than the N-S faults and the water impoundment in the Nasser Lake had caused the groundwater to rise significantly in the Nubian formation near the lake. Consequently, the pore pressure distribution and the stresses in the subsurface units were altered. Seismicity studies revealed that the majority of earthquakes are clustered along the Kalabsha fault zone (KFZ). Hamimi et al. [13] concluded that the KFZ is a complex dextral transtensional shear zone that controls the seismotectonic setting and deforming the southern part of Egypt. Furthermore, they introduced and discussed several structural and geological evidences about the reactivation and ongoing activity of the KFZ.

Figure 1 Location map of the study region (a) and geological-geomorphological and structural setting (b) of the study area modified after, EGSMA (1981).
Figure 1

Location map of the study region (a) and geological-geomorphological and structural setting (b) of the study area modified after, EGSMA (1981).

1.2 Preliminary analysis of Aswan seismicity

The ongoing earthquake sequence that began in 1981, provide a primary source of detailed seismicity data. Extensive recording of seismicity in Aswan region by a rapidly deployed installation and updating of Aswan seismic network (Figure 2a) have produced a detailed picture of the ongoing microseismicity in Aswan region since 1981 until now. Preliminary analysis of Aswan seismicity focused on the seismicity distribution, seismicity depth, and focal mechanism. Earthquakes hypocentral locations were calculated using the computer program Hypoinverse under Atlas software package. The stations’ location, a convenient crustal velocity model, the P-wave and S-wave arrival times are the input to the program. The output files resulted from the processing are used for constructing the seismicity map of the Kalabsha area from 1981 until 2023 (Figure 2b). From the map, we noticed clearly that the seismicity follows the geometry of Lake Nasser. The earthquakes are clustered in definite zones and along specific trends. The dense of seismic activity is concentrated along the KFZ in consistence with the surface trace of the fault. Low activity is observed in the N-S directions along Abu Dirwa Fault and at the intersection of Gabal El-Barqa and Seiyal Faults. It was clearly shown that most of the seismic activity occurred at the intersected zone between the E-W and N-S trending faults and at their end points. During the studied period, the seismicity map (Figure 2c) shows the same features and characterization. The analysis of the earthquakes focal depths distribution led to a very clear discrimination of two depth classes, separated by the threshold of 12 km, which separates earthquakes as shallower from deeper than the threshold depth (Figure 3a and b). Six seismic events with local magnitude ≥ 3.5 are selected to study the mechanism of faulting in the study region, using the first motion polarity of P-wave [14]. Our result indicated strike slip movement with minor normal sense (Figure 4). Table 1 lists the best fault planes solution of the investigated events.

Figure 2 The Aswan seismic network (a), the recorded seismicity from 1981 to 2022 (b) and the selected earthquakes (c).
Figure 2

The Aswan seismic network (a), the recorded seismicity from 1981 to 2022 (b) and the selected earthquakes (c).

Figure 3 The earthquakes depth classification from 1981 to 2022 (a) and during the studied period from 2010 to 2022 (b).
Figure 3

The earthquakes depth classification from 1981 to 2022 (a) and during the studied period from 2010 to 2022 (b).

Figure 4 Six earthquake fault plane solutions established from P-wave first motions for the selected events in the Aswan region.
Figure 4

Six earthquake fault plane solutions established from P-wave first motions for the selected events in the Aswan region.

Table 1

Obtained best solution double couple for Aswan reservoir earthquakes

No. Events ID Plane 1 Plane 2 Depth (km) Mw
yyyymmddhhmm Strike/Dip/Rake Strike/Dip/Rake
1 201003130222 54/72/−154 315/65/−20 12 3.4
2 201011070954 90/60/−125 325/45/−45 4 3.3
3 201112261701 55/75/−180 325/90/−15 9 4.1
4 201310271321 145/85/−20 237/70/−175 4 2.9
5 201402021331 247/85/−160 155/70/−5 3 3.02
6 201609192150 242/85/−155 150/65/−5 3 3.3

1.3 Dataset and data processing

In the light of the current study, we examined 1,200 local seismic events with magnitude 0.8 ≤ M L < 4.2. The selected dataset is considered shallow crustal earthquakes with focal depths ranging from 0 to 30 km. The initial parameters of the selected earthquakes are used to investigate the seismicity distribution, hypocentral depths, path attenuation, station sites response, and advanced source parameters. The studied events are mostly of smaller magnitude 0.8 ≤ M L ≤ 3.0 (98.2%) and only 20 earthquakes have M L > 3.0 (1.8%). The velocity waveform data are stored in SEED format with sampling frequency of 100 Hz. The data selection follows the criteria proposed by Ren et al. [15]. The hypocentral distance of selected earthquakes is limited to 10–100 km, considering that ground motion attenuation depends on both magnitude and distance. Most recordings fall within this range to maintain a relatively uniform distribution. Only earthquakes with a signal-to-noise ratio (S/N) greater than 5 are included in the inversion process. To ensure the reliability of the results, each selected event must be recorded by at least five stations. Ultimately, the dataset for inversion analysis consists of 1,200 earthquakes recorded at 12 seismic stations. The data are processed using seismic analysis code software (Goldstein and Snoke, 2005). The observed seismograms are converted from SEED to SAC format (Figure 5a), and then the arrival time of P-wave in the vertical component and that of S-wave in the two horizontal components are picked. The instrument effect is removed using the poles and zeros file of each seismic station (Figure 5b). The time windows for Fast Fourier Transform (FFT) are set for 1.28 and 2.56 s from P-wave arrival and S-wave one, respectively, and the windows for noise prior to P-wave with the mentioned times (Figure 5c). 1% taper window are applied to the data point, the mean and linear trend are removed, and zero padding is applied. An SNR cutoff was applied to frequencies below 50 Hz, preserving spectra with a mean SNR greater than 10 (Figure 5d). Fourier spectra were then computed, and the amplitude spectrum was derived by averaging the spectra from all three components for both signal and noise (Figure 6a). Finally, the spectra were smoothed using a Parzen window with a 5 Hz bandwidth (Figure 6b).

Figure 5 Data processing sequence illustrating (a) Example of the observed three-component seismograms recorded at the DRWA seismic station for the 19 September 2016 earthquake ( M L = 3.5 {M}_{{\rm{L}}}=3.5 ). (b) Preliminary analysis of the instrument response correction. (c) Example of the cut window of pre-signal noise with a length of 1.28 s, the cut signal window of the P-wave with the same length, and the cut signal window of S-wave with length 2.56 s. (d) Examples of the calculated SNR ratio at each station that recorded the 19 September, 2016 earthquake ( M L = 3.5 {M}_{{\rm{L}}}=3.5 ).
Figure 5

Data processing sequence illustrating (a) Example of the observed three-component seismograms recorded at the DRWA seismic station for the 19 September 2016 earthquake ( M L = 3.5 ). (b) Preliminary analysis of the instrument response correction. (c) Example of the cut window of pre-signal noise with a length of 1.28 s, the cut signal window of the P-wave with the same length, and the cut signal window of S-wave with length 2.56 s. (d) Examples of the calculated SNR ratio at each station that recorded the 19 September, 2016 earthquake ( M L = 3.5 ).

Figure 6 FFT analysis sequence: (a) The Fourier amplitude spectra derived from three-component seismograms for both noise (blue curves) and signal windows (red curves). (b) Example of the smoothed spectra (black solid curve) for the calculated displacement source spectra (red curves) obtained at three component station.
Figure 6

FFT analysis sequence: (a) The Fourier amplitude spectra derived from three-component seismograms for both noise (blue curves) and signal windows (red curves). (b) Example of the smoothed spectra (black solid curve) for the calculated displacement source spectra (red curves) obtained at three component station.

2 Methodology

2.1 Generalized inversion technique (GIT)

The observed seismogram is a contribution of the source, the path, and recording site responses. The principal problem in the source studies is to separate the contribution elements of the recorded seismogram [16]. Andrews [17] first proposed the GIT by suggesting that the observed power spectrum is the multiplication of the source, site, and geometrical spreading. Iwata and Irikura [18] modified the assumption of Andrew and added the inelastic attenuation of seismic waves. Since then, the GIT has been widely applied in the evaluation of attenuation studies [19], site characteristics [20], and the source types [21]. In the frequency domain, the observed spectra O ij ( f ) from the source i and receiver j is the multiplication of the source S i ( f ) , site G j ( f ) , and the path P ij ( f ) .

(1) O ij ( f ) = S i ( f ) G j ( f ) P ij ( f ) .

Iwata and Irikura [18] suggested that the quality factor of attenuation was frequency dependent and then they applied natural logarithm ( ln ) for both members of equation (1) to obtain the following simultaneous linear equation:

(2) ln e i + ln S j + − π ft ij Q ( f ) = ln [ O ij R ij ] .

Construct a linear system of equations to determine Q ( f ) at each frequency using least squares inversion. This process will yield a frequency-dependent attenuation model of the form Q ( f ) = Q 0 f α , w here Q 0 is the reference Q value at 1 Hz, and α is the frequency dependence exponent (attenuation coefficient). Equation (2) can be formulated in matrix form as listed in equation (3), in addition, it can be solved numerically by the iterative least square technique.

(3) [ A ] [ X ] = [ d ] ,

where A is called the Kernel matrix, X is the model parameters, and d is the observed seismogram. Equation (3) has an unconstrained degree of freedom, which led to a trade-off between the site and source results. To suppress this problem, a reference rock site was used. Many researchers consider the amplification at the reference site S ref ( f ) = 2 due to the free surface effect [18]. In the current study, a broadband seismic station named Gabal Marawa (GMR) is selected as a reference site [8]. The obtained displacement amplitude spectra is modulated using Brune model [22] as follows:

(4) e i ( f ) = Ω 0 1 + ( f / f c ) 2 ,

where Ω 0 is proportional to the seismic moment, and f c is the corner frequency. For circular fault model, the seismic moment, the corner frequency, the source radius, the stress drop, and the moment magnitude can be derived from the P-wave or S-wave displacement spectra following previous literature [17,22,23], and the relations are as follows [24]:

(5) M 0 = 4 π ρ v 3 F × R θ φ × G ( r , h ) Ω 0 ,

(6) r = k β f c ,

(7) f c = 1 2 π A 0 Ω 0 ,

(8) ∆ σ = 7 16 M o r 3 ,

(9) M W = 2 3 log 10 M 0 − 10.7 ,

where ρ is the density, v is the P- or S- wave velocity, F is the free surface effect, R θ φ is the radiation pattern for the P-wave or S-wave, and G ( r , h ) is the geometrical spreading effect. r is the fault radius, f c is the observed corner frequency, and k is a constant that depends upon the specific theoretical model.

3 Results

3.1 GIT results

The GIT using a reference site is a powerful method for estimating site, source, and path effects [15]. However, it has notable limitations. The reliability of the results depends significantly on factors such as reference site selection, station distribution, data quality, and data quantity. A key limitation is the dependence on the reference site for event selection, the number of events available for estimating source parameters is restricted to those recorded at the reference site. Earthquakes not captured by this site cannot be included in the inversion, even if other stations have recorded them [25]. Additionally, achieving robust inversion results requires a well-distributed dataset of earthquakes with diverse source characteristics. Addressing these challenges necessitates careful validation and the use of multiple reference sites. The Aswan seismic network has achieved good station coverage using three-component broadband seismometers (Trillium-120 sensors). Three stations were tested as potential reference sites (NWAL, NWKL, and NGMR) based on their flat site response, with NGMR providing the most reliable results, having recorded the majority of the investigated earthquakes. By utilizing over 1,200 events with high quality data and carefully validating the initial source parameters, along with selecting a reference site with a stable amplification curve around 2.0 across the entire frequency range, the robustness and reliability of the inversion process were ensured. Based on the spectral inversion using reference site, the observed spectra are decomposed into the source spectra, local site, and the path effect including attenuation from P- and S-waves displacement amplitude spectra.

3.1.1 Attenuation characteristics

The attenuation of seismic waves amplitude were caused by two effects, the heat loss (seismic absorption), and the scattering of seismic waves (energy redistribution). The attenuation of seismic wave amplitude is most often observed to be frequency dependent in the form Q ( f ) = Q 0 f α in the upper part of the crust [26]. Q For P-wave are smaller than Q for S-wave in the upper crust and Q s / Q p = 1.5 [27]. It is known in the literature that seismically active region has low quality factor value at reference frequency ( Q 0 ) less than 200, and have attenuation coefficient (α) around 1 [28]. Low Q value at lower frequency can result from the movement of fluid in the causative fault zone or/and cracks saturated with fluid [28,29]. Mukhopadhyay et al. [30] investigated the attenuation characteristic of seismic waves in the target area using coda normalization method, they obtained average frequency-dependent relation fitting to Q s = 34.2 f 1.02 .

Aswan region is known to be a region where reservoir-triggered seismicity occurs. In the current study, the determined Q 0 of Aswan seismic region Q 0 = 133 and the value of α = 0.54, using P-wave spectra, and the determined Q 0 of Aswan seismic region Q 0 = 91 and the value of α = 0.8, using S-wave spectra, indicated that the Aswan seismic region belongs to the seismically active region. Furthermore, the low value of Q would mean that the medium is complex and highly heterogeneous. The path effect is expressed as the quality factor of P-wave ( Q p ). It is obtained by the inversion in the range from 428 to 1,386, with average frequency-dependent relation proportional to Q p = ( 133 ± 2.09 ) f ( 0.54 ± 0.034 ) . It indicated that the attenuation is frequency dependent for P-wave as illustrated in Figure 7a and Table S1 (Supplementary material). The quality factor of S-wave ( Q s ) obtained by the inversion is in the range from 430 to 2,344, with average frequency-dependent relation proportional to Q s = ( 91 ± 1.9 ) f ( 0.8 ± 0.045 ) . It is indicated that the attenuation for S-wave has the frequency dependence as illustrated in Figure 7b and Table S1 (Supplementary material). Since the probability values for Q P (0.008) and Q S (0.01) indicate statistical significance at any reasonable significance level (e.g., 0.05) in the ANOVA table, there is a statistically significant relationship between Q and f at the 95% confidence level. The R-squared statistic shows that the fitted model explains 88.8% of the variability in Q for P-waves and 95.1% for S-waves. Additionally, the standard error of the estimate, representing the standard deviation of the residuals, is 0.034 for P-waves and 0.045 for S-waves.

Figure 7 (a) P-wave attenuation model ( Q P {Q}_{{\rm{P}}} ) obtained from inversion, shown as a function of frequency. Red circles represent Q p values on a logarithmic scale, with a reference attenuation value Q O {Q}_{{\rm{O}}} = 133, and a frequency dependence exponent n {n} = 0.54. The black line indicates the linear fit to the data. (b) S-wave attenuation model ( Q S ) \left({Q}_{{\rm{S}}}) derived from inversion, also plotted as a function of frequency. Blue circles represent Q S {Q}_{S} values on a logarithmic scale, with a reference attenuation value Q O = 91 {Q}_{{\rm{O}}}=91\hspace{0.25em} and a frequency dependence exponent n = 0.8 n=0.8 . The black line denotes the linear fit to the data.
Figure 7

(a) P-wave attenuation model ( Q P ) obtained from inversion, shown as a function of frequency. Red circles represent Q p values on a logarithmic scale, with a reference attenuation value Q O = 133, and a frequency dependence exponent n = 0.54. The black line indicates the linear fit to the data. (b) S-wave attenuation model ( Q S ) derived from inversion, also plotted as a function of frequency. Blue circles represent Q S values on a logarithmic scale, with a reference attenuation value Q O = 91 and a frequency dependence exponent n = 0.8 . The black line denotes the linear fit to the data.

Our result for the Q factor in the crust for both P- and S-waves indicated that the Q p value is smaller than Q s value in the lithosphere, i.e., approximately Q s / Q p = 1.2 . The relationship Q 0 f α helps differentiate seismic activity levels, Q O < 200 indicates highly active zones, Q O < 600 corresponds to stable regions, and intermediate values suggest moderate activity [31,32]. Our findings place the Aswan in a seismic region within a tectonically active area characterized by complex structures [33]. This may be due to heterogeneities such as faulting, folding, crustal movements, uneven subsurface geometry, and varying rock properties [34]. Comparing our Q p and Q S values, obtained through inversion analysis of P- and S-wave spectra, with that in the study by Moustafa et al. [35,36,37], we find that their frequency-dependent models, Q P = 11.22 ± 2.2 f 1.09 ± 0.07 and Q s = 9.89 ± 1.89 f 1.14 ± 0.07 , align with our results, though variations arise from differences in frequency range selection. Similarly, Mukhopadhyay et al. [30], using the coda normalization method, derived Q s = 34.2 f 1.02 , with discrepancies attributed to differences in coda window length and frequency range. Further comparisons with studies from tectonically similar regions such as, Central Asia [38], New Madrid [39], East Central Iran [40], Umbria Marche, Italy (de Lorenzo et al., [41]), Andaman Sea [42], Koyna Reservoir, India [28], and Kanto, Japan [43], show consistency with our findings. These studies support the hypothesis that Q p is lower than Q s in the upper crust, with attenuation characteristics mainly controlled by high lateral heterogeneity and the presence of pore fluids.

3.1.2 Site characteristics

Aswan seismic network consists of 13 broadband seismic stations. Two of them are located in the east side of Nasser Lake. The stations are located in a region of low relief. The sedimentary thickness ranges from 100 to 400 m. the surface geology is mainly composed of Nubian sandstone and sediments of age ranging from Late cretaceous to Eocene. The inverted site responses of Aswan seismic network are shown in Figure 8. Our result using P- and S- spectra indicated that the sites responses have flat curves in the low frequency and with amplification deviated from 4 to 10 in the high frequency band from 8 to 50 Hz. DRWA and NGRW stations have peak amplitude of 4 at 10 Hz and flat curve in the frequency band from 0.8 to 8 Hz. NKUR station has peak appearing at 12 Hz and flat response in the frequency band from 0.8 to 8 Hz. NMAN station has amplification around 20 Hz and deamplification in the frequency range from 4 to 7 Hz. To summarize, as illustrated in Figure 6, the S-wave data (red curve) generally exhibit higher amplification than the P-wave data (blue curve). Amplification varies across frequencies, with some stations displaying distinct peaks at specific frequencies. Different seismic stations show unique amplification patterns, some exhibit multiple peaks, while others demonstrate a more gradual increase. The NAHD station shows relatively moderate amplification across most frequencies, whereas NKRL, NWKL, NNMR, NGAL, and NMAN stations experience significant amplification at higher frequencies. Additionally, NGRW, NWAL, NSKD, and NMAN stations have dual peaks, suggesting complex amplification behavior. In the Aswan region, broadband seismic stations often exhibit notable amplitude peaks, each with distinct dominant frequencies. These variations can be attributed to structural heterogeneities and differences in soil and geological conditions across the station sites, such as variations in sediment thickness, rock composition, and fault structures. The site response analysis suggests that local geological factors play a crucial role in shaping seismic wave propagation, influencing ground motion characteristics during earthquakes. The observed high-frequency amplification peaks indicate potential resonance effects, particularly in areas with soft sediments, which could increase seismic risk for infrastructure and buildings. Understanding these site-specific responses is essential for seismic hazard assessment in Aswan region, aiding in the development of more accurate ground motion models and informing engineering practices for earthquake-resistant construction. The findings provide valuable insights for geotechnical investigations, land-use planning, and the mitigation of seismic risks in the region.

Figure 8 The site response analyses from 12 seismic stations, comparing amplification effects for P-wave (blue curves) and S-wave (red curves) data. Each subplot represents a different station, labeled at the top of each graph. A gray rectangular region highlights a specific frequency range at each station, indicating the amplification range of interest. Some stations have black arrows pointing to dual peaks, suggesting complex amplification behavior.
Figure 8

The site response analyses from 12 seismic stations, comparing amplification effects for P-wave (blue curves) and S-wave (red curves) data. Each subplot represents a different station, labeled at the top of each graph. A gray rectangular region highlights a specific frequency range at each station, indicating the amplification range of interest. Some stations have black arrows pointing to dual peaks, suggesting complex amplification behavior.

3.1.3 Source characteristics

Finally, the displacement source spectra for each event are corrected from site and path attenuation effects. It is seen that the P-wave displacement source spectra decreased rapidly at frequency range higher than 20 Hz for earthquakes with magnitude ≤ 3 (Figure 9a), whereas it is attenuated at frequency range less than 10 Hz for earthquakes ≥ 3 (Figure 9b). The same result was obtained for S-wave source spectra (Figure 9c and d). The obtained source spectra is then modulated with theoritical omega-square [22]. The Brune model is a fundamental tool in earthquake seismology for characterizing the spectral properties of seismic sources [44]. It is commonly applied to small to moderate earthquakes, assuming a simple circular rupture and a single corner frequency decay. However, this assumption may not hold for larger earthquakes with complex rupture processes, such as multiple asperities and directivity effects. Additionally, the model presumes an isotropic radiation pattern, overlooking directivity influences in larger events. It also assumes a homogeneous, isotropic elastic half-space, whereas the Earth’s structure exhibits heterogeneities and inelastic behaviors [45,46]. Next the corrected spectrum is used to determine the two essential parameters in earthquakes source studies, namely, the corner frequency and the seismic moment. We determined the relative moment for each displacement source spectra from the mean amplitude in the frequency range from 6.25 to 7.81 Hz.

Figure 9 The stacked displacement source spectra for earthquakes < 3 (a) and for earthquakes ≥ \ge 3 (b), obtained by the spectral inversion of P-wave. The stacked displacement source spectra for earthquakes < 3 (c) and for earthquakes ≥ \ge 3 (d), obtained by the spectral inversion of S-wave.
Figure 9

The stacked displacement source spectra for earthquakes < 3 (a) and for earthquakes ≥ 3 (b), obtained by the spectral inversion of P-wave. The stacked displacement source spectra for earthquakes < 3 (c) and for earthquakes ≥ 3 (d), obtained by the spectral inversion of S-wave.

3.2 Dynamic source parameters results

3.2.1 Seismic moment and moment magnitude

The seismic moment M 0 was calculated as formulated in equation (5) using the observed flat level ( Ω 0 ). The density ρ , P-wave velocity v p , and the S-wave velocity v s used here are taken from the velocity structure model reported in the study by Khalil et al. [47] as shown in Table 2. Radiation pattern corrections of 0.52 and 0.63 were applied for P and S-waves, respectively, following Aki and Richards (2002). The seismic moment obtained for both P- and S-source spectra was in the range from 9.2 × 10 16 to 2.1 × 10 21 dyne-cm and 9.7 × 10 16 to 2.1 × 10 21 dyne-cm, respectively. The computed seismic moment M 0 is converted to the moment magnitude (M w) using [24] empirical equation. The determined moment magnitude shows a linear relationship with the assigned local magnitude. The fitting relation between the local magnitude catalog (Figure 10a and b) and the estimated moment magnitude by a least-squares regression analysis using P- and S-wave source spectra of earthquakes < 3 is controlled by the following equations:

M W − P = ( 0.55 ± 0.014 ) M L + ( 0.8 ± 0.024 ) ,

M W − S = ( 0.57 ± 0.0139 ) M L + ( 0.9 ± 0.025 ) .

Table 2

Velocity structure model (after Khalil et al., [47])

Depth (km) P-velocity (km/s) S-velocity (km/s) Density (g/cm3)
0.0 4.5 2.57 2.6
3.5 6.0 3.42 2.8
16.0 6.5 3.71 3.0
31.0 8.0 4.57 3.2
Figure 10 The calculated P-wave moment magnitude vs the local magnitude M L {M}_{{\rm{L}}} in the catalog. (a) Those from conducting a regression analysis of earthquakes < 3. (b) Those from conducting regression analysis of earthquakes ≥ \ge 3. The calculated S-wave moment magnitude vs the local magnitude M L {M}_{{\rm{L}}} in the catalog. (c) Those from conducting a regression analysis of earthquakes < 3. (d) Those from conducting regression analysis of earthquakes ≥ \ge 3.
Figure 10

The calculated P-wave moment magnitude vs the local magnitude M L in the catalog. (a) Those from conducting a regression analysis of earthquakes < 3. (b) Those from conducting regression analysis of earthquakes ≥ 3. The calculated S-wave moment magnitude vs the local magnitude M L in the catalog. (c) Those from conducting a regression analysis of earthquakes < 3. (d) Those from conducting regression analysis of earthquakes ≥ 3.

The fitting relation between the local magnitude catalog (Figure 10c and d) and the estimated moment magnitude by a least-squares regression analysis using P- and S-wave source spectra of earthquakes ≥ 3 is controlled by the following equations:

M W − P = ( 1.3 ± 0.17 ) M L − ( 1.5 ± 0.57 ) ,

M W − S = ( 1.1 ± 0.14 ) M L − ( 0.54 ± 0.41 ) .

3.2.2 Corner frequency and source radius

The corner frequency ( f c ) in the present study ranged from 4 to 36 Hz and from 4 to 25.5 Hz for P- and S-waves, respectively. The obtained range of the f c is reasonable for the investigated earthquakes magnitude. The plotting of the seismic moment in logarithmic scale vs the logarithm of the corner frequency (Figure 11a) indicated that the corner frequency for P-wave decreased with increasing seismic moment and their linear relation obtained by a least-squares regression analysis is empirically formulated as

Log M O − P = ( − 2.7 ± 0.137 ) Log f C − P + ( 21.4 ± 0.108 ) .

Figure 11 Regression analysis result of seismic moment vs the corner frequency in logarithmic scale using P-spectra (a) and S-spectra (b).
Figure 11

Regression analysis result of seismic moment vs the corner frequency in logarithmic scale using P-spectra (a) and S-spectra (b).

The plotting of Log M o vs Log f c (Figure 11b) indicated that the corner frequency for S-wave decreased with increase in the seismic moment and their linear relation obtained by a least-squares regression analysis is formulated empirically as

Log M O − S = ( − 3.5 ± 0.125 ) Log f C − S + ( 22 ± 0.140 ) .

From the above empirical equation, we can note that the scaling relation between the seismic moment and the corner frequency obtained in the present study is proportional to M o α f c − p − 2.7 and M o α f c − s − 3.5 for P- and S-waves, repectively. It is well known in the literature that earthquakes self-similarity follows the scaling law M o α f c − 3 . Figure 12 shows the fitting relation between f c − p and f c − s , and their regression relation is controlled by f c P ≃ 1.1 f c S for the studied seismic events in Aswan region. The obtained fitting relation is comparable with the theoretical formula reported by Madariaga [48]. Our result is consistent with previous worldwide cases [49,50], which showed that the observed ratios f c P / f c S range from 1 to 2. Also, our result is consistent with the model reported by Madariaga [48], who predicted f c P was about 50% higher than f c S . In general, f c P is consistently higher than f c S because the V P is higher than V S . As a consequence, due to interference effects f c P is higher than f c S [49].

Figure 12 Regression analysis result of the corner frequency in logarithmic scale using P-spectra and S-spectra.
Figure 12

Regression analysis result of the corner frequency in logarithmic scale using P-spectra and S-spectra.

The dynamic model reported by Madariaga [48] related the source radius ( r O ) to the obtained corner frequency. Here the value of the constant k is taken to be 0.32 and 0.21 for P- and S-waves, respectively, and the shear wave velocity β is taken to be 3,400 m/s. The determined source dimension for earthquakes used in this study range from 30 to 190 m and from 28 to 167 m for P- and S-waves, respectively. Plotting of log seismic moment against logrithm source radius for P-wave displacement source spectra (Figure 13a) shows a linear relation obtained by a least-squares regression analysis controlled by the following emperical equation,

Log M O − P = ( 2.7 ± 0.108 ) Log r O − P + ( 13.13 ± 0.194 ) .

Figure 13 Regression analysis result of the seismic moment vs the source radius in logarithmic scale using P-spectra (a) and S-spectra (b).
Figure 13

Regression analysis result of the seismic moment vs the source radius in logarithmic scale using P-spectra (a) and S-spectra (b).

Plotting of log source radius against log seismic moment for S-wave displacement source spectra (Figure 13b) shows a linear relation obtained by a least-squares regression analysis controlled by the following empirical equation:

Log M O − S = ( 3.5 ± 0.125 ) Log r O − S + ( 12.13 ± 0.217 ) .

3.2.3 Determination of the stress drop

Stress drop values are investigated to deeply study the dynamic of the triggered seismicity in Aswan region. The obtained seismic moment ( M 0 ) and crack radii ( r 0 ), for P-spectra r 0 P and S-spectra r 0 S are used to calculate the stress drop following the relation given by Eshelby [23] for Aswan earthquakes assuming circular rupture. The observed stress drops for triggered earthquakes ∆ σ P , obtained from the inversion of P-wave, varies from 0.01 to 12 MPa with an average of 0.17 MPa, and the stress drop ∆ σ S ranged from 0.03 to 22 MPa with an average of 0.41 MPa for earthquakes with local magnitude ranging from 0.8 to 4.2. The low stress drop values obtained for Aswan earthquakes may be attributed to several factors such as the high pore pressure, the crack filled with water, frequently and repeated earthquakes in the same patches, and the swarm characteristic of Aswan seismicity. Consequently, the mentioned factors decrease the friction coefficient of the causative faults in the study region. The impoundment of large reservoir modifies the tectonic stress regime either by increasing the pore pressure or by increasing the loading stress (vertical stress), which results in decreasing the normal effective stress [51]. Reservoir triggered seismicity (RTS) included a complex interaction between normal stress, shear stress, and pore pressure diffusion [52]. Hough [53] suggested that the combination effects of load and water percolation increases the pore pressure and decreases the shear stress. Furthermore, the cohesion and friction coefficient may decrease due to water lubrication after the reservoir impoundment. Hough [53] concluded that the RTS has lower stress drop value than tectonic one. Our result may corroborate the hypothesis that the reservoir triggered earthquakes appeared to have lower stress drop than the tectonic earthquakes as proved in many studies [53,54,55]. Our estimated stress drop values for Aswan earthquakes match well with that in the previous studies in the same target area [2,3,4,8]. Hassib [2] reported stress drop values ranging from 0.02 to 14.2 MPa for Aswan earthquakes with local magnitudes between 1.5 and 3.1. Mohamed [3] found a narrower range, 0.05–7.0 MPa for earthquakes with magnitudes between 1.9 and 3.4. Similarly, El-Amin [4] estimated stress drops between 0.03 and 3.0 MPa for events of magnitude 2.0–3.4, while Saadalla et al. [8] reported values from 0.03 to 2.9 MPa for earthquakes with magnitudes ranging from 1.4 to 3.3. The obtained stress drop values are consistent with global observations in similar tectonic regions. For example, a mean stress drop of 0.1 MPa for seismicity in the Koyna Dam area, India was reported in previous studies [28,54], while Fletcher et al. [56] documented stress drops between 1.3 and 9.2 MPa for seismic events at the Monticello Reservoir, South Carolina. In general, stress drops for triggered earthquakes range from 0.02 to 22 MPa, with a median of 0.3 MPa. In contrast, natural earthquakes tend to exhibit significantly higher stress drops, ranging from 0.9 to 87 MPa, with a median of 7.5 MPa [57,58,59]. Table 3 summarizes the obtained dynamic source parameters for earthquakes with M L ≥ 3.0 using P- and S-displacement source spectra.

Table 3

Calculated dynamic source parameters obtained for P- and S-wave spectra of earthquakes ≥ 3.0 in Aswan region

Event ID f CP (Hz) f CS (Hz) M OP (dyne-cm) M OS (dyne.cm) M L M WP M WS r P (m) r S (m) ∆ σ P (MPa) ∆ σ S (MPa)
201003130222 5.9 6.1 6.6 × 1020 4.8 × 1020 3.8 3.7 3.6 185 117 4.57 13.0
201006220822 14.3 8.3 1.1 × 1019 3.4 × 1019 3.2 2.5 2.8 76 86 1.11 2.3
201011070954 7.4 6.0 2.7 × 1020 4.6 × 1020 3.8 3.4 3.5 148 118 3.68 12.2
201011160237 7.2 6.9 6.3 × 1019 8.7 × 1019 3.2 3.0 3.1 151 103 0.80 3.5
201011301023 9 8.5 4.6 × 1019 3.8 × 1019 3 2.7 2.8 120 84 1.6 2.7
201104140653 23.9 9.6 5.6 × 1018 1.9 × 1019 3.1 2.3 2.6 46 75 2.60 2.0
201104140653 17.8 9.0 1.6 × 1019 5.7 × 1019 3.2 2.6 2.9 61 80 3.12 4.9
201106130808 16.3 8.5 4.7 × 1019 1.7 × 1020 3.3 2.9 3.3 67 84 6.94 12.5
201112151032 8.7 8.7 2.9 × 1020 2.9 × 1020 3.3 3.4 3.4 82 82 11.3 23.0
201112261701 4.3 4.3 2.1 × 1021 2.1 × 1021 4.2 4.0 4.0 167 167 12.4 19.7
201305171039 12.1 9.7 2.0 × 1019 2.1 × 1019 3.23 2.6 2.6 90 74 1.18 2.3
201306180926 14.6 12.8 1.4 × 1019 1.3 × 1019 3.09 2.5 2.5 74 56 1.50 3.4
201306191621 12.3 10.3 2.2 × 1019 3.7 × 1019 3.12 2.7 2.8 89 69 1.40 4.8
201310271321 8.6 6.7 9.4 × 1019 1.5 × 1020 3.38 3.1 3.2 127 106 2.00 5.6
201410181055 9.2 7.0 2.1 × 1019 6.3 × 1019 3.1 2.7 3.0 118 103 0.56 2.5
201410182202 8.5 8.2 6.0 × 1019 4.0 × 1019 3 2.6 2.8 112 87 1.4 2.6
201511211047 9.6 6.1 6.9 × 1019 1.8 × 1020 3.2 3.0 3.3 113 116 2.09 4.9
201511211053 11.5 5.7 1.5 × 1019 9.7 × 1019 3.1 2.5 3.1 94 126 0.76 2.1
201609192150 5.8 4.9 2.1 × 1020 3.3 × 1020 3.5 3.3 3.5 187 146 1.40 4.7
201609211235 7.4 5.8 8.0 × 1019 1.2 × 1020 3.4 3.0 3.2 147 123 1.11 2.9

f CP : P-wave corner frequency, f CS : S-wave corner frequency, M OP : P-wave seismic moment, M OS : S-wave seismic moment, M WP : P-wave moment magnitude, M WS : S-wave moment magnitude, r P : P-wave source radius, r S : S-wave source radius, Δ σ P : P-wave stress drop, Δ σ S : S-wave stress drop, M L : Local magnitude.

Figure 14a and b shows a contour map of the spatial distribution of stress drop ∆ σ over the study area for P- and S-waves, where higher stress drop value was concentrated in the area outside the lake margins in the crustal region, where the earthquakes occur infrequently (Table 4).

Figure 14 Stress drop ∆ σ \triangle \sigma distribution of the selected earthquakes in Aswan seismic zone using P-spectra (a) and S-spectra (b).
Figure 14

Stress drop ∆ σ distribution of the selected earthquakes in Aswan seismic zone using P-spectra (a) and S-spectra (b).

Table 4

Source parameters estimated in previous studies for Aswan region

Author Magnitude (ML) Seismic moment (dyne-cm) Corner frequency (Hz) Source radius (M) Stress drop (MPa) Moment magnitude ( M w )
El Amin (2011) 2.0–3.54 0.97 × 1020–7.52 × 1020 7.23– 9.06 277–344 0.03–2.98 1.9–3.54
Mohamed (2004) 1.5–3.1 4.37 × 1018–2.2 × 1021 7.1–11.9 91.4–312 0.05–7.0 1.55–2.86
Hassib (1997) 1.9–3.4 4.9 × 1018–8.1 × 1021 7–20 178–377 0.02–14.2 —
Saadalla et al. (2019) 0.9–3.5 1.5 × 10 17 – 2.1 × 10 20 6–34 34–194 0.03–2.9 1.4–3.3
Current study 0.8–4.2 9.2 × 10 16 – 2.1 × 10 21 4–36 25–195 0.01–22 1.1–4.0

4 Discussion

This study applies the GIT to estimate site, source, and path effects in the Aswan seismic region. While GIT effectively decomposes observed spectra into source, site, and path contributions, its accuracy relies on careful reference site selection, data quality, and event distribution. The Aswan seismic network, with well-distributed broadband stations, identified NGMR as the most reliable reference site, ensuring robust spectral inversion results. Seismic wave attenuation, influenced by heat loss and scattering, exhibits frequency dependence, with Q P lower than Q S , indicating higher attenuation in P-waves. The Aswan region, with Q 0 values of 133 (P-wave) and 91 (S-wave), confirms its classification as a tectonically active zone. The observed frequency-dependent attenuation aligns with global studies, reinforcing the role of structural heterogeneity and fluid-filled cracks in controlling seismic wave propagation. Site response analysis reveals station-dependent amplification, with some sites showing distinct resonance peaks. Variations in amplification patterns highlight the influence of local geology, sediment thickness, and fault structures on ground motion characteristics. The findings emphasize the importance of site-specific hazard assessment for infrastructure planning. Source characteristics derived from spectral inversion confirm that smaller earthquakes ( ≤ 3 magnitude) exhibit higher-frequency attenuation, while larger events attenuate at lower frequencies. The source spectra align with the model reported by Brune [22], though deviations suggest complex rupture processes.

The seismic moment was derived using spectral parameters, with values ranging from 9.2 × 10 16 to 2.1 × 10 21 and 9.7 × 10 16 to 2.1 × 10 21 dyne-cm for both P- and S-waves, respectively. The moment magnitude was then estimated using empirical relation from the study by Kanamori [24]. Regression analysis established a clear correlation between local magnitude and moment magnitude, revealing a change in scaling behavior for earthquakes above and below magnitude 3.0. This shift suggests different rupture mechanics for smaller and larger events in the Aswan region, which is consistent with global observations of source scaling. Corner frequency values ranged from 4 to 36 Hz for P-waves and 4–25.5 Hz for S-waves. Regression analysis confirmed that f C decreases as seismic moment increases, following the empirical relationships, M o α f c − p − 2.7 and M o α f c − s − 3.5 , for P-and S-waves, respectively. This trend deviates slightly from the classical self-similarity scaling law, possibly due to regional stress conditions or fluid-induced rupture processes. The ratio f c P ≃ 1.1 f c S , aligns with previous global studies, indicating that P-wave corner frequencies remain higher due to differences in wave velocity and interference effects. The estimated source radii ranged from 30 to 190 m (P-wave) and 28 to 167 m (S-wave), supporting a size-dependent rupture behavior. Stress drop values varied significantly, from 0.01 to 12 MPa (P-waves) and from 0.03 to 22 MPa (S-waves), with average values of 0.17 and 0.41 MPa, respectively. The lower stress drop values suggest RTS, influenced by high pore pressure, water-filled cracks, and repeated seismic activity in the same fault patches. These findings align with global RTS studies, where stress drop values are generally lower than those of purely tectonic earthquakes.

The spatial distribution of stress drop revealed higher values concentrated outside the lake margins in the deeper crust, where seismicity is less frequent. This distribution indicates that stress conditions and pore pressure variations control rupture dynamics in the Aswan region. The findings match previous studies in Aswan and broader RTS case studies, such as Koyna, India, and Monticello Reservoir, USA. Overall, the study establishes clear linkages between seismic source parameters, stress drop variations, and the role of reservoir-induced stress changes. The combination of low stress drop values, frequency-dependent attenuation, and site-specific amplification effects highlights the need for refined hazard assessment models in Aswan. Understanding these seismic characteristics is crucial for infrastructure resilience and seismic risk mitigation in regions affected by anthropogenic stress changes.

5 Conclusion

  • The comprehensive analysis of seismicity in the Aswan region, supported by the continuous expansion and modernization of the Aswan seismic network, has provided a detailed understanding of the microseismicity activity since 1981. The recorded seismic events, primarily concentrated along fault zones inundated by Lake Nasser, highlight the influence of reservoir-triggered seismicity. The focal depths of these earthquakes range from 0 to 30 km, with deeper events localized beneath the Gabal Marawa seismic zone. Fault plane solutions indicate that strike-slip motion with minor normal faulting is the dominant mechanism governing seismic activity in the area.

  • Seismic wave attenuation in the region follows a frequency-dependent relationship, with low Q values suggesting a highly heterogeneous crust and fault zones saturated with water. The site response analysis further reveals significant amplification at high frequencies (8–50 Hz), underscoring the complex seismic wave propagation characteristics in Aswan.

  • The inversion of source spectra has allowed for the estimation of critical seismic parameters, including seismic moment, corner frequency, source radius, and stress drop, using Brune’s (1970) model. The derived scaling relationships suggest that seismic moment is proportional to the source radius and inversely proportional to the corner frequency, consistent with global observations. Stress drop values in Aswan range from 0.01 to 12 MPa for P-waves and 0.03 to 22 MPa for S-waves, with lower averages than typical tectonic earthquakes. This supports the hypothesis that reservoir-triggered seismicity tends to exhibit lower stress drops, likely due to high pore pressure, water saturation, and repeated small-scale fault movements. Importantly, stress drop values generally increase with moment magnitude but show no strong correlation with source depth.

  • The integration of seismicity patterns, attenuation characteristics, site response, and source parameters is essential for seismic hazard assessment in Aswan. The GIT proves to be a powerful tool for distinguishing source, path, and site effects, improving regional seismic hazard models. It aids in refining the regional quality factor for hazard assessment and identifying zones of significant site amplification, which is crucial for updating building codes and guiding structural retrofitting efforts. Additionally, the evaluation of dynamic source parameters such as stress drop, rupture area, and moment magnitude provide key insights into earthquake rupture behavior. Understanding whether local faults exhibit frequent small earthquakes with low stress drops or occasional large ruptures is vital for assessing future seismic hazards.

  • A multidisciplinary approach integrating seismology, geodesy, and tectonic studies is necessary for a comprehensive understanding of the Aswan region’s seismogenic potential. Such an approach enhances probabilistic seismic hazard analysis, identifies faults capable of generating significant earthquakes, and strengthens early warning systems. The findings from this study contribute to real-time earthquake monitoring, ground motion prediction, and mitigation strategies, ultimately reducing seismic risk in the region.

Acknowledgments

Our deep gratitude goes to our organization, Aswan Earthquake Research Center, ENSN Lab, Seismology département, National Research Institute of Astronomy and Geophysics (NRIAG), for providing us with the earthquakes data used in this study. Also, authors would like to express their gratitude to the Researchers Supporting Project number (RSP2025R432), King Saud University, Riyadh, Saudi Arabia.

  1. Funding information: The authors state no funding involved.

  2. Author contributions: Hamada Saadalla and Takumi Hayashida processed the earthquake data. Saleh Qaysi, Hamada Saadalla, and Mona Hamada contributed to the focal mechanism analysis and reviewed the manuscript. Abdula Abdelnabi and Takumi Hayashida. worked on the spectral inversion analysis and revised the introduction and geology sections. Saleh Qaysi and Hamada Saadalla contributed to the revised version of the manuscript and addressed the reviewers’ comments. All authors participated in the writing, revision, and review of the article, as well as in the interpretation and integration of the results. All authors have read and approved the final version of the manuscript.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Ethics approval and consent to participate: Not applicable.

  5. Consent for publication: Not applicable.

  6. Data availability statement: The data that support the findings of this study are available from NRIAG but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission from NRIAG.

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Received: 2024-11-05
Revised: 2025-03-26
Accepted: 2025-03-27
Published Online: 2025-05-02

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2025-0795
Keywords for this article
Aswan seismicity; P- and S-waves spectral inversion; attenuation; and site characterization
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. Seismic response and damage model analysis of rocky slopes with weak interlayers
  3. Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
  4. Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
  5. GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
  6. Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
  7. Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
  8. Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
  9. Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
  10. The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
  11. Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
  12. Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
  13. Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
  14. Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
  15. Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
  16. Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
  17. Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
  18. Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
  19. Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
  20. Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
  21. Research on the variation in the Shields curve of silt initiation
  22. Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
  23. Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
  24. Analysis of the formation process of a natural fertilizer in the loess area
  25. Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
  26. Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
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  28. Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
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  31. Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
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  34. Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
  35. Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
  36. Predicting coastal variations in non-storm conditions with machine learning
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  41. Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
  42. Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
  43. Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
  44. Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
  45. Identification of radial drainage networks based on topographic and geometric features
  46. Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
  47. Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
  48. Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
  49. Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
  50. Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
  51. An efficient network for object detection in scale-imbalanced remote sensing images
  52. Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
  53. Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
  54. A modified Hoek–Brown model considering softening effects and its applications
  55. Evaluation of engineering properties of soil for sustainable urban development
  56. The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
  57. Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
  58. Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
  59. Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
  60. 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
  61. Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
  62. Land use classification through fusion of remote sensing images and multi-source data
  63. Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
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  65. Factors impacting spatial distribution of black and odorous water bodies in Hebei
  66. Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
  67. Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
  68. Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
  69. Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
  70. Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
  71. Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
  72. Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
  73. Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
  74. Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
  75. Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
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  77. Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
  78. New age constraints of the LGM onset in the Bohemian Forest – Central Europe
  79. Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
  80. Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
  81. Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
  82. Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
  83. Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
  84. Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
  85. Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
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  87. Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
  88. Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
  89. A test site case study on the long-term behavior of geotextile tubes
  90. An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
  91. Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
  92. Comparative effects of olivine and sand on KOH-treated clayey soil
  93. YOLO-MC: An algorithm for early forest fire recognition based on drone image
  94. Earthquake building damage classification based on full suite of Sentinel-1 features
  95. Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
  96. Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
  97. An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
  98. Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
  99. Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
  100. Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
  101. A metaverse-based visual analysis approach for 3D reservoir models
  102. Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
  103. Integrated well-seismic analysis of sedimentary facies distribution: A case study from the Mesoproterozoic, Ordos Basin, China
  104. Study on the spatial equilibrium of cultural and tourism resources in Macao, China
  105. Urban road surface condition detecting and integrating based on the mobile sensing framework with multi-modal sensors
  106. Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data
  107. The synergistic use of AHP and GIS to assess factors driving forest fire potential in a peat swamp forest in Thailand
  108. Dynamic response analysis and comprehensive evaluation of cement-improved aeolian sand roadbed
  109. Rock control on evolution of Khorat Cuesta, Khorat UNESCO Geopark, Northeastern Thailand
  110. Gradient response mechanism of carbon storage: Spatiotemporal analysis of economic-ecological dimensions based on hybrid machine learning
  111. Comparison of several seismic active earth pressure calculation methods for retaining structures
  112. Review Articles
  113. Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
  114. Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
  115. Ore-controlling structures of granite-related uranium deposits in South China: A review
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