# Fault structure and earthquake clustering in Aswan region (Egypt) revealed by high-precision earthquake location from 35 years of recorded natural and induced seismicity

Tony Alfredo Stabile, Elshafey Raafat Fat-Helbary, Vincenzo Serlenga, Serena Panebianco, Pietro Tizzani, Raffaele Castaldo, Luciano Telesca, Ezzat Mohamed El-Amin, Ahmed Hamed

<figure><img src="image_1.png" /></figure>

## To cite this version:

Tony Alfredo Stabile, Elshafey Raafat Fat-Helbary, Vincenzo Serlenga, Serena Panebianco, Pietro Tizzani, et al., Fault structure and earthquake clustering in Aswan region (Egypt) revealed by high-precision earthquake location from 35 years of recorded natural and induced seismicity. Earth and Planetary Science Letters, 2024, 642, pp.118881. 10.1016/j.epsl.2024.118881. hal-04795474

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https://doi.org/10.1016/j.epsl.2024.118881

Fault structure and earthquake clustering in Aswan region (Egypt) revealed by
high-precision earthquake location from 35 years of recorded natural and
induced seismicity

Tony Alfredo Stabile a,*, Elshafey Raafat Fat-Helbary b, Vincenzo Serlenga a, Serena Panebianco a, Pietro Tizzani c, Raffaele Castaldo c, Luciano Telesca a, Ezzat Mohamed El-Amin b, Ahmed Hamed b

a Institute of Methodologies for Environmental Analysis, National Research Council (CNR-IMAA), Tito Scalo (PZ), 85050, Italy

b National Research Institute of Astronomy and Geophysics, 11421 Helwan, Cairo, Egypt

c Institute for Electromagnetic Sensing of the Environment, National Research Council (CNR-IREA), Naples, 80124, Italy

* Corresponding author. E-mail address: [tonyalfredo.stabile@cnr.it](mailto:tonyalfredo.stabile@cnr.it) (T.A. Stabile).

Highlights

* A new 1-D V<sub>p</sub> and V<sub>s</sub> velocity model of the Aswan region has been determined.

- High-precision earthquake locations of the seismicity in Aswan have been performed.

* Spatiotemporal evolution of seismicity reveals a progressive activation of faults.

* The seismicity is highly clustered.

* A seismic gap along the Kalabsha fault suggests a possible locked fault patch.
**Abstract**

Starting from an initial catalogue of 7833 natural and reservoir-induced seismic events collected in Aswan region (south Egypt) from 1982 to 2016, we investigate the fault structure and triggering mechanisms by determining high-resolution hypocenters for 2562 earthquakes. Despite the complex network of intersecting fractures, high-precision earthquake locations reveal numerous discrete fault strands (some not mapped yet), whose kinematics have been determined from the focal mechanisms of Ml ≥ 2.5 earthquakes. Furthermore, the analysis of the space-time evolution of seismicity indicates an eastward migration of earthquakes and a progressive activation of different faults. This process could reflect fluid migration in the Wadi Kalabsha embayment likely controlled by permeability barriers at depth acting as seals, and the subsequent build-up of the pore pressure as the dominant driving mechanism of the observed seismicity in the region. A strong spatio-temporal earthquake clustering is observed in the region characterized by seismic sequences, repeated earthquakes, and long-lasting swarms. High variability of b-value in correspondence of the occurrence of mainshock-aftershock sequences also suggests the activation of faults and fractures within their respective fault zones under lower differential stress conditions due to fluids. The projection of earthquake hypocenters on an E-W vertical depth section of the study area shows a seismic gap with an approximate length of about 11 km along the Kalabsha fault, which suggests the presence of a locked fault patch that may generate a Mw 5.9 earthquake if this segment were to break in a single rupture episode.

**Keywords**

Aswan region; induced seismicity; high-precision earthquake location; fault structure; earthquake clustering;
b-value changes

**1. Introduction**

The Aswan High Dam, built between 1960 and 1971, with its height of 111 m and a crest length of 3830 m is the largest water resource in Egypt, impounding the second largest reservoir in the world, the Lake Nasser, whose gross capacity is of 169 billion cubic meters. Since November 14, 1981, when a Ms 5.3 earthquake occurred south of the dam, the dam stability related to the seismicity of the area became a crucial issue in the
context of the reservoir management. Thus, in 1982 a seismic network was established (Simpson et al., 1987), and since then an intense seismic activity has been continuously recorded (Simpson et al., 1990; Gahalaut et al., 2016, and references therein).

Several studies focused on the relationship between the Aswan reservoir water level and the observed seismicity in the region (e.g., Kebeasy et al., 1981; Simpson et al., 1990; Hassoup 1994; Selim et al., 2002; Mekkawi et al., 2004; Haggag et al., 2008; Telesca et al., 2012; Gahalaut et al., 2016; Telesca et al., 2017). Although in some time periods the influence of the reservoir on the seismicity occurrence was not clearly assessed (Hassoup 1994; Selim et al., 2002; Mekkawi et al., 2004; Telesca et al., 2012), it is commonly accepted that seismicity of Aswan area represents a typical case of continued/protracted reservoir induced seismicity (RIS), similar to several other worldwide sites such as Lake Mead in the USA (Carder, 1945), Koyna-Warna reservoir in India (Gupta 2002; Durá-Gómez and Talwani, 2010), Nurek Dam in Tajikistan (Simpson and Negmatullaev, 1981), Açu reservoir in Brazil (El Hariri et al., 2010), and Pertusillo reservoir in Italy (Stabile et al., 2014; Telesca et al., 2015), where induced seismicity was observed yearly or after a gap of a few years. Such delayed seismic response to water fluctuations of reservoirs indicates that the diffusion of pore fluid pressure dominates the mechanism of the observed continued RIS. For the Aswan area, in particular, Gahalaut and Hassoup (2012) showed through analytical simulations that, although the stress due to the reservoir load stabilizes the seismogenic faults of the area, the pore fluid pressure effectively leads the faults to go beyond the critical stress for failure. Telesca et al. (2017), by applying time-clustering and scaling analysis statistical methods to the Aswan seismicity, found that the loading/unloading operations at Lake Nasser reservoir might have a significant effect in triggering both the shallow and deep earthquakes. Despite several studies having been carried out to investigate the relationship between seismicity and water level changes of Lake Nasser, none of them focused on the accurate imaging of the fine-scale fault structure of such a complex system. In this study we relocate with high-precision for the first time the seismicity recorded between 1982 and 2016 in the Aswan region, to better understand the distribution, geometry, and kinematics of active faults and to study the spatio-temporal evolution and clustering of earthquakes along these faults yielding insights into their triggering mechanisms.
## 2. Seismotectonic settings of Aswan region and data description

Aswan is a region in the south of Egypt (Fig. 1) and represents a stable shelf of clastic and carbonate rocks accreted to the crystalline Arabian-Nubian Shield that is skirted by a belt of the unstable shelf of deformed sequences of shallow- and deep-water sediments (Youseff, 2003). The surface geology consists of about 500 m thick Nubian sandstone and sediments of age ranging from the Late Cretaceous to the Eocene (Gahalaut and Hassoup, 2012). The Quaternary formations are represented by calcite and Nile deposits (Issawi, 1978, 1982). Geomorphologically, the area around Aswan is almost flat with relief of about 200 m.

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Overview of the known faults in Aswan region (south of Egypt) and seismicity provided by the Aswan seismic catalogue. Earthquakes are displayed with circles having radius proportional to the magnitude whereas their color indicates the hypocentral depth. Seismic stations of the Aswan seismic network are represented with blue triangles.</figcaption></figure>
The entire region is characterized by a conjugate set of almost vertical E-W and N-S trending faults, which extend into the granitic basement and intersect beneath the Gebel Marawa area (Issawi, 1978, 1982; Kebeasy et al., 1987; Gahalaut and Hassoup, 2012). The right-lateral strike-slip movement is the dominant mechanism associated with the E-W trending Kalabsha fault zone, along which the November 14, 1981, Ms 5.3 earthquake occurred. The Seiyal fault, located almost 12 km north of the Kalabsha fault (Fig. 1), is also an E-W trending fault with right-lateral strike-slip movement. All together they constitute an E-W graben structure occupied by the Wadi Kalabsha embayment (Gahalaut and Hassoup, 2012), where most of the Aswan seismicity is concentrated (Fig. 1). The dominant mechanism associated with the N-S trending fault segments is instead a left-lateral strike-slip movement. Both right lateral faults (E-W striking) and left lateral faults (N-S striking) are consistent with the current stress regime of the area, being Shmax and Shmin oriented WNW-ESE and NNE-SSW, respectively (Bosworth and Strecker, 1997; Hussein et al., 2013). The N-S trending faults north of the embayment area are named, from west to east, the Gebel El-Barqa, Kurkur, Khour El-Ramla, and Spillway faults. The latter is near the High Dam and was activated by the November 7, 2010, Ml 4.6 earthquake and successive aftershocks (Fat-Helbary et al., 2012; Dahy, 2012; Mohamed et al., 2022; El-Bohoty et al., 2023). The N-S trending faults south of the embayment area are the Gazelle and Abu Dirwa faults (Fig. 1). Finally, on the east side of the Lake Nasser reservoir and just south of the Aswan dam lies the NW-SE trending Dabud fault (Fig. 1).

Prior to the November 14, 1981, Ms 5.3 earthquake, no earthquake within 200 km has been reported in the International Seismological Center (ISC) catalog (Kebeasy et al., 1987). Although the operation of seismic stations in the study area was irregular prior to 1981, 20 events of magnitude > 2.5 have been identified by Helwan Institute located in the Wadi Kalabsha area (Telesca et al., 2017). Following the Ms 5.3 earthquake, a network of temporary stations was installed in the northern part of the reservoir area. In late June 1982 the temporary network was replaced by a permanent network of 8 seismic stations deployed by the Helwan Observatory and Lamont-Doherty Geological Observatory (USA). In December 1982, the number of stations increased to nine and in 1985 the network increased to 13 stations, all of them having vertical single components except for two stations having three components (GMR and GRW). In 2009, the Aswan seismic network (Fig. 1) has been updated by replacing the short-period seismographs with broadband three-component seismometers and by transmitting digital data via a satellite system. After the November 7, 2010,
Ml 4.6 earthquake, occurred along the Spillway fault, the additional ASW station has been installed north of the Aswan High Dam (Fig. 1).

Thanks to the deployment of the Aswan seismic network, a seismic catalogue of the area has been compiled since January 1st, 1982. It includes hypocentral parameters (Latitude, Longitude, depth), event origin time, and the local magnitude Ml of each earthquake. Since the earthquakes reported in the catalogue have been located over time using different 1-D velocity models and different software based on linearized location methods, we did not use the Aswan catalogue's locations in this study. We extracted P- and S-wave travel times from the original files, which were produced for each event, to first perform non-linear absolute location of seismicity in the new 1D velocity model of the area. Subsequently, we refined earthquake location through the double-difference algorithm (see Section 3.3).

### 3. Data analysis and methods

The present study, focused on 35 years of recorded natural and induced seismicity in the Aswan region (period 1982-2016), retrieves a new 1-D velocity model ($V_P$ and $V_S$) and provides high-precision earthquake locations using the same new velocity model and the same earthquake location procedure for the whole recorded seismicity. First of all, we produced a uniform P- and S-wave arrival time dataset of the Aswan seismic network (the “picks.hypo” ASCII file in Hypo71 format is available in the Supplementary material) starting from the information retrieved from the different original output files of each recorded seismic event. The dataset consisted of 42586 P- and 31837 S-phases of 7833 earthquakes, including the information on the first motion P-wave polarity, when available.

#### 3.1 Refined 1-D velocity model of the area

Thanks to the availability of a huge volume of seismic data in the period 1982-2016, recorded by seismic stations deployed in a very wide area, we retrieved a new 1-D velocity model ($V_P$ and $V_S$) of the investigated region. It improves the already existing 1-D velocity models that were simply retrieved from the analysis of earthquake data spanning a shorter time period and seismic refraction profiles (Simpson et al., 1990; Kebeasy et al., 1991; Gharib, 1992; El-Hady et al., 2004).
To refine the 1-D velocity model of the area, the original 1982-2016 seismic dataset was selected based on the following criteria: azimuthal gap lower than 180°, at least six P- phases and three S- phases for each event, RMS lower than 0.5 s, horizontal and vertical location errors lower than 10 km and a maximum residual value for each seismic phase of 2 s. In that way, we obtained a final database consisting of 2042 earthquakes, with 16843 and 14305 P- and S-phases, respectively, which were inverted through a linearized, iterative, damped least-square inversion algorithm implemented in the VELEST code (Kissling, 1995). It simultaneously solves the “coupled-hypocenter-velocity model problem”, allowing for the inversion of the station corrections as well. To this purpose, we chose the station GMR to be the reference station for three reasons: a) it was found having low values of station residuals (El-Hady et al., 2004); b) it is located toward both the center of the seismic network and the main distribution of seismic events; c) it recorded a meaningful number of the seismic phases belonging to the final dataset with a very good temporal continuity in the time period considered in our study.

We preliminarily followed a two-stage procedure (thorough details in Supplementary Text S1) that allowed us to assess the independence of the final solution on both the starting velocity model and the chosen subset of inverted data.

To achieve our final purpose, we strictly followed the workflow proposed by Kissling et al. (1994). First, among the five subsets of data initially created, we inverted the one providing the minimum RMS values at the initial single-step inversion (Supplementary Text S1): we thus obtained a new 1D a-priori velocity model, by first combining adjacent layers with similar velocities and then running a new inversion which was stopped when the earthquake locations and the velocity values did not change significantly in subsequent iterations. Then, we relocated the whole dataset in the updated a priori 1D model with the station residuals obtained at the final step. Finally, we inverted the travel-time data belonging to the best earthquakes in terms of azimuthal gap, number of observations and distance of the closest station to the hypocenter. The final 1D P-wave and S-wave velocity models (Supplementary Fig. S4), station residuals and hypocentral parameters corresponded to the solution of our inverse problem, whose robustness has been assessed through proper synthetic tests (Supplementary Text S2). The optimum 1D velocity model along with the updated earthquake locations and station corrections allowed a reduction of P- and S-wave residuals of the 33% with respect to the crustal model adopted for the initial earthquake locations.
## 3.2. Completeness magnitude and time variation of the b-value using the β⁺ estimator

The completeness magnitude of the Aswan seismic catalogue and its time variation has been deeply investigated by Telesca et al. (2017): they proposed a reasonable conservative choice of the completeness magnitude (Mc = 2.5) as the largest value of the time evolution of Mc, whose variation was due to the several upgrades of the Aswan seismic network which certainly influenced the earthquake detection capability. Since the classical estimate of the b-value depends on the completeness magnitude, to obtain a reliable estimate of b through time, b(t), in the study area, we used the b-positive estimator proposed by van der Elst (2021) which is insensitive to transient changes in catalog completeness:

$$b = \beta^{+}(\ln 10)^{-1} = (\overline{m' - M'_{c}})^{-1}(\ln 10)^{-1}; \quad m' \geq M'_{c}$$

where β⁺ is the b-positive estimator, $\overline{m'}$ is the sample mean of the positive magnitude differences greater than $M'_{c}$, and $M'_{c}$ is the minimum magnitude difference (0.2 units in this study).

The b-positive estimator, and consequently the b-value, has been estimated for a running window of 70 positive magnitude differences greater than $M'_{c}$.

In order to compare b(t) with the monthly seismic moment rate, we converted the local magnitude Ml of events in moment magnitude Mw using the relationship Mw = 0.6 Ml + 0.8 proposed for the Aswan region by Saadalla et al. (2019), and then obtaining the seismic moment Mo from Mw using the classical relationship of Hanks and Kanamori (1979).

## 3.3. Absolute and relative earthquake location

The linearization of earthquake location process, which is a non-linear inversion problem, was preferred in the past due to its faster computation with respect to non-linear approaches. The continuous improvement of hardware performance over the years has allowed an extensive use of non-linear algorithms, for which the final solution do not depend on an initial trial solution (e.g., see Lomax et al., 2014). Moreover, several authors (e.g., Thurber 1985; Lomax et al., 2000; Presti et al., 2008; Lomax et al. 2014) compared the performances of iterative-linearized location algorithms and non-linear, global search location algorithms. These studies demonstrate that the former produce lower quality location error estimates compared to the latter.
For the above-mentioned reasons, in this study we applied a non-linear location method (NonLinLoc, Lomax et al., 2000) by using the optimized 1-D velocity model to improve earthquake absolute locations with respect to those reported in the Aswan seismic catalog. We adopted a two-step absolute location procedure, with the second run using the residuals obtained from the first run. In this way, 7809 events were relocated in the Aswan region with resulting RMS values less than 0.4 s, with a median of the RMS distribution equal to 0.05 s.

With the aim to obtain an accurate imaging of the fault structure of the investigated area through seismicity, we performed relative earthquake locations using the double-difference method (implemented in the HypoDD code; Waldhauser and Ellsworth, 2000) by computing a total number of 422,931 P- and S-waves differential times among events satisfying the following criteria: 1) maximum hypocentral separation of 10 km for each earthquake pair; 2) minimum number of 10 links required to define a neighbor; 3) for each event, a maximum number of 10 neighbors has been saved; 4) maximum number of 30 saved links for each earthquake pair. The initial events satisfying the differential time match were 2818. The inversion procedure has been performed using the same optimized 1-D velocity model of the absolute locations and has been solved using the conjugate gradient method (LSQR, Paige and Saunders, 1982) implemented in HypoDD. Indeed, this approach takes advantage of the sparseness of the system of DD-equations and can solve a large system efficiently (Waldhauser and Ellsworth, 2000), such as in this case study. A damping factor of 60 has been chosen to lower as much as possible the condition number of the DD system, as suggested by Waldhauser and Ellsworth (2000), and taking into account that larger numbers of earthquakes generally produce larger condition numbers, as in our case. At the end of the inversion process, only the earthquakes relocated with RMS lower than 0.1 s and with a maximum distance of 5 km between linked pairs were selected. A total dataset of 2562 double-difference relocated earthquakes was obtained ("hypoDD.reloc.mag.csv" CSV file available in the Supplementary material). The 99th percentile of the horizontal and the vertical relative location error distributions were 115 m and 204 m, respectively.

### 3.4. Focal mechanism estimation

Given the limited data of P-wave first motion polarities, focal mechanisms were determined using the FPFIT code (Reasenberg and Oppenheimer, 1985). The inversion was accomplished through a grid search step of
one degree. Fault plane solutions were exclusively computed for relocated earthquakes with local magnitude $Ml \ge 2.5$, and for which at least 10 P-wave first motion polarities were available. The solutions were retained only if the station distribution ratio (STDR) exceeds 0.5. In instance where a seismicity cluster was identified, the composite focal mechanism was also determined using the FPFIT code, considering all available P-wave first motion polarities from events within the cluster.

## 3.5. Clustering analysis

Clustering techniques are a powerful unsupervised machine-learning tool that allows exploring dataset structures and recognize internal patterns within it. Indeed, the purpose of clustering algorithms is to identify groups or clusters characterized by internal homogeneity with respect to the explored parameters (features).
We applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN; Ester et al., 1996).
It is a density-based clustering algorithm that explores observations' space aiming to find clusters from the estimated density distribution of their corresponding nodes. For this reason, DBSCAN can find clusters of any shape and does not need any a-priori assumption on the number of clusters to identify within the dataset.
The learning process of the algorithm, based on the notion of "ε-neighborhood" and "density-reachability" (Ansari et al., 2011), requires two hyperparameters: (1) the distance within which to search for close points (ε); (2) the minimum number of samples to create a cluster (n). The ε parameter controls the local neighborhood of the points so that an appropriate data-driven value should be selected: for too small ε-values no clusters are found; conversely, too large ε-values would cause close clusters to be merged. The n parameter, instead, primarily controls how tolerant the algorithm is towards noise.

We applied a grid search to different hyperparameters combinations ($0.1 \le \epsilon \le 1.9$, step = 0.1) and ($0 \le n \le 50$, step = 5) to obtain the hyperparameter couple which maximized the clustering performance within the input dataset, being the best compromise between the following calculated metrics: (a) number of clusters found; (b) mean noise points distance from the closest 6 points (6-N); (c) number of identified noise points (outliers). The method allowed us to choose the values of ε and n that jointly minimize the number of outliers and maximize the 6-N distance for the observed signals.
**3.6. Finite Element Poroelastic model**

To simulate the effects of the water level variations on the considered faults, we performed a 3-D poroelastic model in a Finite Element (FE) environment. In particular, we solved for a time-dependent poroelastic equation and linear elastic one taking into account a Multiphysics approach by using Comsol software package v 6.2 (Comsol, 2023). The model geometry extends for 100 km x 110 km and 25 km along depth (Supplementary Fig. S6); it includes the DEM of the area and the faults' geometries. The fully saturated poroelastic domain ranges from sea level to depths of 6 km while the elastic one ranges from 6 km of depth to the model bottom. The elastic parameters as density, Young's modulus and Poisson's ratio were estimated from the 1-D $V_p$ and $V_s$ velocity model (Supplementary Fig. S4), and the poroelastic parameters, permeability, and porosity, were equal to 1e-14 m² and 0.3 for the storage, respectively, and 1e-11 m² and 0.4 for the fault elements, respectively. The Biot-coefficient was set to 0.8. The FE model geometry was discretized by using 1.2 millions of nodes with a finer refinement on the faults surface. Before the time-dependent analysis, we modelled the effect of lithostatic stress and rocks compaction. The zero-time of the performed modelling corresponds to 1980 and the simulation evolves for 25 years, with a time-step equal to 0.25 years.

**4. Results and discussion**

**4.1 Space-time evolution of seismicity and fault structure of the study area**

Refined locations obtained by applying the double difference method allowed us to better define the fault structure in Aswan region with respect to locations reported in the original Aswan seismic catalogue (see Supplementary Fig. S7) and to better follow the spatio-temporal evolution of seismicity.

Until 1984 the seismicity was characterized by the aftershocks of the 1981 Ms 5.3 earthquake located in the portion of the Kalabsha fault west of the Wadi Kalabsha embayment and with hypocenters deeper than 15 km (Fig. 2a). From 1984 to 1997 (Fig. 2b) the seismicity started to migrate towards east and with hypocenters shallower than 15 km, confirming the observations from previous works (e.g., Mekkawi et al., 2004; Gahalaut and Hossoup, 2012; Telesca et al., 2017). From 1997 to 2004 (Fig. 2c) different patches along the Kalabsha fault have been activated, particularly in correspondence of the intersection between the
Kalabsha fault and the N-S striking Khour El-Ramla faults. In addition, it can be observed the onset of seismicity along the Abu-Dirwa fault in the south and a cluster of seismicity in correspondence of the intersection between the Seiyal fault and the N-S striking Kurkur fault. From 2004 to 2007 (Fig. 2d) it can be observed a seismicity cluster west of the intersection between the Kalabsha and Gazelle faults where the 1981 Ms 5.3 earthquake and its aftershocks occurred, but at shallower depths in the range of 6-11 km.

Furthermore, it is evident the onset of an intense seismic activity on the northern strand of the Kalabsha fault between the Kurkur fault and the Khour El-Ramhla fault with hypocentres from 5 to 9 km depth (Figs. 2d and 3b).

<figure><img src="image_3.png" /><figcaption>**Figure 2:** Spatio-temporal evolution of high precision relocated earthquakes (events color-coded by hypocentral depth) in Aswan region until the end of (a) 1984, (b) 1997, (c) 2004, (d) 2007, (e) 2012, and (f) 2016. It is possible to observe an eastward migration of earthquakes with hypocenters shallower than 15 km and a progressive activation of different faults.</figcaption></figure>
<figure><img src="image_4.png" /></figure>

**Figure 3:** (a) Map view of the relocated seismicity in Aswan along with focal mechanisms of Ml ≥ 2.5 earthquakes. Black solid lines represent the known faults whereas pink dashed lines show the unmapped fault strands identified through seismicity. The red dashed rectangle shows in detail the focal mechanisms computed for events belonging to the complex network of intersecting faults identified east of the Seiyal fault. (b) Vertical depth section of seismicity along latitude which allows us to clearly observe the E-W striking vertical faults. (c) Vertical depth section of seismicity along longitude showing a seismic gap along the Kalabsha fault (locked fault patch?) with an approximate length of about 11 km. (d) The same section as (c) with the contour lines indicating the stress due to the presence of the modelled ellipsoidal locked region.
After 2007 (Figs. 2e and 2f) the seismicity mainly occurred in the Wadi Kalabsha embayment area between the E-W striking Kalabsha and Seiyal faults and the N-S striking Kurkur and Khour El-Ramla faults, thus allowing the identification of unmapped fault strands (pink dashed lines in Fig. 3a) on the east termination of the Kalabsha fault (visible on the N-S vertical depth section in Fig. 3b), with a clear E-W striking vertical fault strand identified by earthquakes having latitude in the range 23.54 ° - 23.56 °N, longitude in the range 32.72 ° -32.74 °E, and depth down to 10 km. An additional onset of seismicity can be observed after 2012 (Fig. 2e and Fig. 3b) along the Kalabsha fault with hypocentral depth between 10 and 12 km. Furthermore, a new NW-SE trending fault in the embayment area ranging from the Kurkur Fault to the Khour El-Ramla fault (NW-SE trending pink dashed line in Fig. 3a) has been identified, whose trend is almost similar to the Dabud fault located on the east side of the Lake Nasser (see Fig. 1). Finally, a possible small fault segment west of the northern part of the Kurkur fault has been identified by some events that occurred in the last period of investigation (Figs. 2f and 3).

We determined focal mechanisms of all relocated events with local magnitude Ml ≥ 2.5 and for which at least 10 first motion P-wave polarities are available, with the aim to have constraints on the kinematics of faults (Fig. 3). The focal solutions of events along the new NW-SE trending fault confirmed a fault strike of about 320° and suggest a dominant left-lateral kinematics. A complex network of intersecting faults has been identified east of the Seiyal fault with focal mechanism ranging from dominant strike-slip kinematics to E-W striking normal faults (red dotted box in Fig. 3a). A possible explanation of the activation of a complex network of shallow fractures (depth of hypocenters less than 6 km) with variable faulting style can be attributed to the combined effect of Coulomb stress and pore pressure perturbations caused by the water reservoir, which is commonly observed in other RIS case studies (e.g., Catchings et al., 2015; Stabile et al., 2015; Li and Luo, 2022). Gahalaut and Hassoup (2012) computed the changes in Coulomb stress due to the reservoir impoundment and in the presence of the tectonic stress on the causative fault planes. They demonstrated that the reservoir load produces a stabilizing effect on faults while pore pressure pushes the faults towards destabilization. On these grounds we could hypothesize that, particularly in this region, the magnitude of the vertical stress Sv may have locally increased from 1998 to 2000 (when normal fault focal mechanisms have been observed in the red dotted box in Fig. 3a) to values exceeding the magnitude of the maximum horizontal stress Shmax due to the reservoir loading without destabilizing the E-W striking faults.
The subsequent increase of pore pressure at depth could have promoted in this period the activation of faults favorably oriented with the temporary local normal stress regime.

<figure><img src="image_5.png" /><figcaption>**Figure 4:** (a) Time series of Lake Nasser's water level above sea level in the time interval from 1982 to 2005. (b) Modelled time series of the pore fluid pressure (blue curve) and vertical component of deformation (green curve) at a specific point within the poroelastic domain, with coordinates E:480200, N:2619400, UTM WGS84 zone 36N. (c) Vertical profile representing the modelled difference between the effective vertical and maximum horizontal stress components $\Delta(\sigma_v-\sigma_H)$ during the 18th year (blue curve) and 19th year (red curve) of the simulation.</figcaption></figure>

To verify our hypothesis, we computed with the FE poroelastic model the displacement and spatial stress field distribution nearby the complex network of intersecting faults east of the Seiyal fault (point within the poroelastic domain at coordinates E:480200, N:2619400, UTM WGS84 zone 36N). The variations of the groundwater table are modelled by considering as input the 1982-2005 lake level time series (Fig. 4a). The fluctuation of the water table level over time implies a change in fluid pressures: specifically, the increase in fluid pressure leads to a positive volumetric deformation, caused by the expansion of the matrix rocks. The model results (Fig. 4b) show a vertical deformation trend of about -0.12 cm/year, between years 2–8,
reaching a value of the simulation subsidence of about 0.73 cm over the 8th year; this value is founded in
correspondence of the lower value of pore fluid pressure and of the minimal groundwater level. After this
subsidence, an uplift deformation trend is observed; the maximum value of 0.2 cm a.s.l. is reached in the 22nd
year of simulation.

We modelled the stress along a vertical profile to investigate the active role of groundwater variation on the
normal faults triggering along the complex intersecting faults systems between 1998 and 2000 (i.e., years 18-
20 for modelling reference time), when the events with normal fault kinematics occurred. Figure 4c shows
the difference between the effective vertical and maximum horizontal stress components Δ(σ<sub>V</sub>−σ<sub>H</sub>); the
achieved results reveal a sudden change in the stress tensor distribution with an increase of its vertical
component respect to the horizontal one. Between the 18<sup>th</sup> and the 19<sup>th</sup> year of simulation we observe a
maximum increase of 0.8 kPa localized between 2 and 4.5 km depth, thus justifying in this period
earthquakes with normal fault kinematics.

The projection of high-precision earthquake locations along the E-W vertical depth section in Fig. 3c shows
a seismic gap along the Kalabsha fault which suggests the presence of a locked fault patch with an
approximate length of about 11 km (violet region in Fig. 3c). High number of clustered earthquakes can be
observed in correspondence of the edges of such patch (see density map in Fig. 4) since the stress
accumulates particularly at the edge of the stuck patch (as also modelled by Perrin et al. (2019) for the San
Andreas Fault near Parkfield in California). To fully investigate these features of the Kalabsha fault, we have
modelled the existence of a locked patch along the structure, starting from the same setting as the 3D
poroelastic model described above. The simulation has been done by considering an ellipsoidal region within
the FE domain characterized by high values of elastic parameters to model the different rheological behavior
of this area. Specifically, by increasing the density and Young's modulus values of about 35-40% respect to
the 1-D reference model, our FE model reveals a stress concentration around the high rigidity body over
time. We point out that the modelled stress values are proportional to the arbitrary percentage increase in the
elastic parameters of the considered media. The results support the hypothesis that stress is distributed in the
surrounding of the high mechanical strength zone, where clustered earthquakes with low magnitude are
observed (see subsection 4.2). According to the empirical laws proposed by Wells and Coppersmith (1994),
we estimated that the locked patch may generate in the future a Mw 5.9 earthquake if this segment were to
break in a single rupture episode. Earthquakes of magnitude >5 are not uncommon in the study region, as it has previously experienced a Ms 5.3 earthquake on November 14, 1981 (e.g., Gahalaut and Hassoup, 2012; Telesca et al., 2017).

High-precision locations reported on map and projected along NS and EW vertical depth sections in Fig. 3b and 4c also evidenced the importance of the improvement of the azimuthal coverage of the Aswan seismic network during time, and particularly around the Wadi Kalabsha embayment area. Indeed, despite double-difference locations have been applied, the seismic events occurred before 1985 were not well constrained thus resulting as a more scattered seismicity with respect to events occurred after 1985 which, on the contrary, well delineate the architecture of the faults structure.

An animated map view of the year-by-year spatio-temporal evolution of Aswan seismicity is available in the Supplementary material (see Supplementary Video 1).

## 4.2 Earthquake clustering

Density map of the number of high-precision relocated events in Aswan region (Fig. 5) suggests earthquake clustering along several patches of the Kalabsha fault system and in correspondence of the intersection among the E-W striking faults and the N-S striking faults of the area. Therefore, we performed a spatial clustering (also including magnitudes) using the DBSCAN algorithm on the whole relocated seismicity by fixing the ε-neighborhood hyperparameter to 0.6 and considering n = 20 (with n the minimum number of events to create a cluster). Three different clusters have been identified with this choice of hyperparameters:

- cluster A characterized by events' depths > 15 km (orange dots in supplementary Fig. S8), which are
  prevalently the aftershocks of the 1981 Ms 5.3 earthquake;

- cluster B characterized by events' depths < 15 km (green dots in supplementary Fig. S8), which
  mainly represent the earthquakes occurred in the Wadi Kalabsha embayment area;

- cluster C characterized by events' latitudes lower than 23.45 °N (red dots in supplementary Fig. S8),
  which are the events occurred along the N-S striking Abu-Dirwa fault.

The 2010 Ml 4.6 earthquake and its aftershocks, occurred close to the Aswan High Dam along the Spillway fault, were classified as outliers (cyan dots identified as "cluster -1" in supplementary Fig. S8), because of
the poor seismic network coverage north of the Aswan High Dam before that date and of the consequent
small number of high-precision relocated events.

<figure><img src="image_6.png" /><figcaption>**Figure 5:** Density map of relocated earthquakes in Aswan suggesting earthquake clustering along several patches of the Kalabsha fault system and in correspondence of the intersection between the E-W striking faults and the N-S striking faults of the area. Particularly, high number of clustered events can be observed in correspondence of the edges of the hypothesized locked patch along the Kalabsha fault.</figcaption></figure>

With the aim to better identify earthquake clustering within the cluster B (i.e., the earthquakes occurred in the Wadi Kalabsha embayment area), we applied the DBSCAN algorithm on these selected events leaving $ε=0.6$, increasing the minimum number of events to create a cluster to *n* = 35, and considering the origin time, instead of the magnitude, as a feature (together with the hypocentral coordinates of events) (Fig. 6b).

The results of such cluster analysis allowed the identification of the following 6 clusters (Figs. 6a and 6b):

- cluster 0: events belonging to the complex network of intersected faults east of the Seiyal fault and along the new identified NW-SE trending fault; they have latitudes ranging between 23.63 °N and 23.67 °N, longitudes between 32.66 °E and 32.7 °E, and hypocentral depths shallower than 6 km (events displayed with orange dots in Fig. 6);

- cluster 1: events occurred along the different fault strands of the Kalabsha fault system; they have latitudes in the range 23.55 °- 23.62 °N, longitudes in the range 32.65 ° - 32.8 °E, and depths < 10 km (green dots in Fig. 6);
- cluster 2: events located in correspondence of the intersection between the Seiyal and Kurkur faults with latitudes in the range 23.66 ° - 23.7 °N, longitudes in the range 32.62 ° - 32.66 °E, depths < 6 km (red dots in Fig. 6);

- cluster 3: events with latitudes in the range 23.45 ° - 23.56 °N, longitudes in the range 32.59 ° - 32.61 °E, and depths between 6 and 11 km (black dots in Fig. 6); they are located on the western edge of the hypothesized locked fault patch in correspondence of the intersection between the Kalabsha and Gazelle faults;

- cluster 4: events with latitudes in the range 23.57°-23.58 °N, longitudes in the range 32.66 -32.7 °E, and depths between 6 and 10 km (dark blue dots in Fig. 6); they are located along a northern strand of the Kalabsha fault system just in correspondence of the locked fault patch;

- cluster 5: events with latitudes in the range 23.54 ° - 23.56 °N, longitudes in the range 32.72 ° -32.74 °E, and depths between 10 and 12 km (purple dots in Fig. 6); they are located on the eastern edge of the hypothesized locked fault patch in correspondence of the intersection between the Kalabsha and Khour El-Ramla faults.

<figure><img src="image_7.png" /><figcaption>Figure 6: (a) Map representation of the different clusters obtained from the DBSCAN clustering algorithm with ε=0.6 and n=35. Composite focal mechanisms corresponding to each seismicity cluster are also shown. (b) Pairplot of the analyzed seismicity with the kernel density estimate of each seismicity feature plotted over the main diagonal.</figcaption></figure>
We also computed the composite focal mechanisms for the six identified clusters, obtaining prevalent strike-slip kinematics with P and T axes consistent with the orientation of the maximum Shmax and minimum Shmin stress, respectively. For the three southernmost clusters (clusters 3, 4, and 5) located along the Kalabsha fault a small normal component can be observed on their composite focal mechanisms (Fig. 6).

<figure><img src="image_8.png" /><figcaption>**Figure 7:** Time distribution of seismicity (including magnitude information) for (a) cluster 0, (b) cluster 1, (c) cluster 2, (d) cluster 3, (e) cluster 4, and (f) cluster 5. Clusters 0 and 1 exhibit swarm type distribution whereas clusters 2-5 can be classified as (foreshock-mainshock-aftershock seismic sequences. The onset of cluster 5 at the end of 2012 was as a mainshock-aftershock sequence but it continued as a swarm until the end of the investigated period.</figcaption></figure>

Looking at the temporal distribution of the six identified clusters (Fig. 7), we can observe that clusters 0 and 1 exhibit swarm-type distribution (Figs. 7a and 7b) whereas clusters 2-5 can be classified as (foreshock-mainshock-aftershock seismic sequences (Figs. 7c-7f). Generally, swarms are thought to be primarily driven by external processes such as fluids or aseismic slip rather than earthquake triggering via stress transfer (Miller et al., 2004; Ross et al., 2020). On these grounds we can hypothesize that clusters 0 and 1 are principally driven by pore pressure diffusion processes due to the impoundment of the water reservoir, with no earthquake in the sequence that can be considered as the mainshock. Such clusters can be defined as continued or protracted RIS occurring in shallow and high fractured regions, with cluster 0 being active from 1984 to 2008 and cluster 1 being still active since 1988. On the other hand, clusters 2-5 may indicate small asperities ($Ml \le 3.5$) located in correspondence of the intersection of E-W and N-S striking faults (e.g., cluster 2) or at the edge of fault segments (e.g., cluster 4) and locked regions (e.g., clusters 3 and 5) where
the stress accumulates during years (Fig. 3d) and is released in a short time as seismic sequences. Fig. 7e shows that at least 3 low-magnitude seismic sequences (in 2002, 2007, and 2014) and some repeated earthquakes occurred in correspondence of cluster 4; furthermore, Fig. 7f shows that after the occurrence of a seismic sequence at the end of 2012, the earthquake distribution of cluster 5 started to have a swarm-type behavior. This observation suggests the build-up of the pore pressure down to 10 and 12 km depths (i.e., the hypocentral depths of the events belonging to the cluster 5) east of the hypothesized locked region; therefore, we suggest paying attention to the seismicity evolution in this area.

Figure 8 shows the *b*-value through time, *b*(*t*), in the study area against the monthly seismic moment rate.

We can observe a nearly constant *b*-value of about 0.7-0.8 from 1985 to 2007, the period when the seismicity is mainly characterized by the swarm activity of clusters 0 and 1 (Figs. 7a and 7b), and after 2013, when cluster 5 started to have a swarm-type behavior. On the contrary, the largest variations of *b*-value are observed from 1982 to 1985 during the aftershocks activity of the November 14, 1981, Ms 5.3 earthquake ($0.7 \le b(t) \le 1.1$), and from 2007 to 2012 ($0.7 \le b(t) \le 1.2$) when (foreshock-)mainshock-aftershock seismic sequences occurred in different regions of the study area (clusters 2-4, and Ml 4.6 earthquake sequence along the Spillway fault). Such additional observations could suggest the presence of permeability barriers at depth acting as sealing, which control the migration of fluids (Ross et al., 2020). As the fluids circumvented the barriers, the largest earthquakes and their aftershocks may occur and an increase of the *b*-value is observed because the build-up of pore pressure promote failure of faults and fractures within their respective fault zone at lower differential stress (e.g., see also Gulia et al., 2018; Stabile et al. 2021).

<figure><img src="image_9.png" /><figcaption>**Figure 8:** Temporal variation of *b*-value (red solid line) along with the standard error of its estimate (red shaded area) against the seismic moment rate of events reported in the Aswan seismic catalogue from 1982 to 2016.</figcaption></figure>
**5. Conclusions**

This study investigated the fault structure and earthquake clustering in Aswan region (south Egypt) by collecting 42586 P- and 31837 S-wave arrival times of 7833 earthquakes occurred in the study area from 1982 to 2016. A total dataset of 2562 high-precision relocated earthquakes with the double-difference method was obtained using a new 1-D velocity model (V<sub>P</sub> and V<sub>S</sub>) expressly developed to reduce travel-time residuals.

The use of high-precision earthquake locations has significantly improved our ability to track the eastward migration of seismic activity over time. We also identified additional fault strands within the Kalabsha fault system and a new NW-SE trending fault in the embayment area, extending from the Kurkur Fault to the Khour El-Ramla fault. Moreover, our observations have led to the hypothesis of a locked fault patch approximately 11 km in length. To simulate the presence of this locked fault patch, we employed an ellipsoidal shape, increasing density and Young's modulus values to around 35-40% higher than those in the 1-D reference model. The simulations revealed a notable increase in stress at the patch's edges, reaching up to 10 MPa, where clusters 3, 4, and 5 have been observed (Figure 3d). The combined effect of stress increase and the gradual build-up of pore-pressure over time raises concerns about potential future failure of the fault patch. In the event of a single rupture episode, there is a possibility of a magnitude Mw 5.9 earthquake.

This research opens up new perspectives on the seismic hazard in the study area, highlighting the pivotal role of fluid-fault structure interaction in seismic triggering processes. The impoundment of Lake Nasser allows fluids to migrate downwards, and the subsequent increase of pore-pressure emerges as the driving process behind earthquake swarms (clusters 0 and 1), characterized by a constant b-value of about 0.7-0.8. The complex network of faults allows fluid migration eastward, but it is controlled by permeability barriers at depth acting as seals. When fluids circumvented the barriers and reach the intersection of faults, or the edge of fault segments, or locked regions, where the stress accumulates during years, faults and fractures within their respective fault zones are activated under lower differential stress conditions. In such scenarios, mainshock-aftershock seismic sequences occur, some of which are preceded by foreshock activity. This dynamic process also contributes to the highest variability in b-values.

Based on these considerations, we suggest paying more attention to the seismicity evolution and the temporal variation of b-value within the study area, particularly along the Kalabsha fault system where a locked fault
patch is hypothesized and modelled. This objective can be achieved by starting to save continuous data
streams of the Aswan seismic network and implementing advanced earthquake detection procedures (e.g.,
Yoon et al., 2015; Poiata et al., 2016; Ross et al. 2019; Mousavi et al., 2020; Stabile et al., 2021).

Furthermore, we emphasize the importance of conducting a tomography study to gain comprehensive
insights into the subsurface structure and 3D lateral heterogeneities of the study area, including fluid-driven
changes in medium properties over time.

Acknowledgements

The authors acknowledge financial support from 2018-2019 CNR/ASRT common project. The authors also
acknowledge financial support under the National Recovery and Resilience Plan (NRRP), Mission 4,
Component 2, Investment 1.1, program PRIN 2022, Decree No. 104 published on 2/2/2022 by the Italian
Ministry of University and Research (MUR), funded by the European Union - NextGenerationEU - Project
"Multiscale study of seismogenic processes in Campania-Lucania Apennines using machine learning
algorithms and multiparametric observations (FRACTURES)" - CUP B53D23006980006 - Grant Number
2022BEKFN2.

We are grateful to the Editor Jean-Philippe Avouac and the two reviewers, Ole Kaven and the anonymous
referee #1, for their thorough review of the manuscript and insightful comments.

Data availability

Data of P- and S-wave arrival times and the catalogue of high-precision relocated earthquakes are available
in the Supplementary material. Data of the original Aswan seismic catalogue, the water level time series, and
the GPS measurements will be made available on request by writing to Fat-Helbary Elshafey Raafat (email:
[fat_helbary2@yahoo.com](mailto:fat_helbary2@yahoo.com)).

CRediT authorship contribution statement

**Tony Alfredo Stabile:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Elshafey Raafat Fat-Helbary:** Writing – review & editing,
Validation, Resources, Project administration, Data curation. **Vincenzo Serlenga:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis. **Serena Panebianco:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis. **Pietro Tizzani:** Writing – review & editing, Visualization, Validation, Software, Methodology, Investigation, Formal analysis. **Raffaele Castaldo:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis. **Luciano Telesca:** Writing – review & editing, Writing – original draft, Validation, Methodology, Conceptualization. **Ezzat Mohamed El-Amin:** Writing – review & editing, Resources, Data curation. **Hamed Ahmed:** Writing – review & editing, Resources, Data curation.

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Supplementary materials**

Supplementary material associated with this article can be found, in the online version, at

[doi:10.1016/j.epsl.2024.118881](https://doi.org/10.1016/j.epsl.2024.118881).

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