1 Introduction

The Aswan High Dam region in southwestern Egypt is located on the African Plate within the Nubian Shield craton. Most of the active faults in western Aswan that are related to the Western Desert fault system are oriented east–west and few faults are north–south oriented (Fig. 1a). The east–west faults are located adjacent to the Sinn El Kaddab plateau and are relatively long, extending to the Nubia plain; some extend more than 200 km (Issawi 1973; Issawi 1978; Woodward-Clyde Consultants 1985; Hamimi et al. 2018). The most active faults are distributed in the Kalabsha fault zone (Hamimi et al. 2018), and the largest was the magnitude 5.6 earthquake of November 14, 1981. Former studies in Aswan have shown that seismic activity is concentrated along the active faults and increases near the intersection of north–south and east–west faults (Fig. 1b). Most earthquakes have a strike-slip focal mechanism, while normal fault motion is rarely observed (e.g., El-Amin 2003; Abou-Elenean 2003; Hassib et.al 2010; Hussein et al. 2013; Hosny et al. 2014; Badreldin et al. 2019; Saadalla et al. 2020; Mostafa and Mohamed 2021).

Fig. 1
figure 1

A Geological setting of the Aswan region (modified after EGSMA 1981). B Distribution of earthquakes and quarry blasts (circles) recorded between January 2010 and December 2021. The size of the circle corresponds to the size of the event. Triangles indicate seismic observation stations

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The cement industry in Egypt is growing rapidly in Aswan since limestone, the primary raw material for cement, is mined in the Sin el-Khadab plateau. The blasting of many quarries in the area has yielded 70% of the limestone used in the cement industry. The quarrying activity has been recorded by the local Aswan seismic sub-network, operated by the National Egyptian Seismic Network (ENSN). The anthropogenic activity decreases the reliability of the earthquake catalog in the target region, which leads to incorrect earthquake hazard and risk analysis. Additionally, these quarry blasts are carried out in an area with high seismic activity and complex tectonic setting. The explosions may trigger earthquakes in the nearby faults and disturb the stress pattern of existing tectonic structures (Fat-Helbary et al. 2012). Quarry blasting near the seismically active areas has fostered the interest of researchers to identify the characteristics of seismic and quarry activity in the Aswan region.

Discrimination of anthropogenic activities from natural earthquakes is done using simple procedures such as explosion location, times of occurrence, and satellite images. However, the methods may not be robust and not be reliable for explosions occurring in active fault zones (Wiemer and Baer 2000; Horasan et al. 2009). The discrimination can also be achieved by applying waveform-based approaches, including spectral modulation (e.g., Taylor et al. 1988; Kim et al. 1994; Walter et al. 1995), amplitude ratios among seismic phases (e.g., Bennett and Murphy 1986; Wuester 1993; Plafcan et al. 1997; McLaughlin et al. 2004; Morozov 2008, Anderson et al. 2009; Dahy and Hassib 2010), coda wave analysis (Su et al. 1991; Hartse et al. 1995), ripple-fire spectral modulation schemes (e.g., Hedlin et al. 1990; Smith 1993; Carr and Garbin 1998; Arrowsmith et al. 2006, 2007), and other procedures (Musil and Plesinger 1996; Parolai et al. 2002; Tibuleac et al. 2004).

This study aims to differentiate the characteristics of quarry blasts from earthquakes in Aswan. We investigated the spectral properties of ripple-firing quarry blasts and earthquakes in the southwestern part of Aswan, Southern Egypt, to quantify the differences in spectral characteristics between natural earthquakes and blasts. This discrimination helps to provide a reliable seismic catalog containing only earthquakes in the study area, which can evaluate the seismicity of seismic zones in and around Aswan.

2 Data

The Aswan seismic sub-network, operated by the Aswan Regional Seismological Research Centre of the National Research Institute of Astronomy and Geophysics (NRIAG), consists of 15 broadband seismic stations. The seismometers installed in the network are Trillium 40 and Trillium-120QA (Nanometrics Inc.), and the data were recorded with a sampling frequency of 100 Hz. We processed 3069 signals from 301 shallow earthquakes (< 5 km depth) and 112 quarry blasts recorded by the sub-network in southern Egypt from January 2010 to December 2021. Since the objective of this study is to investigate the differences between blasting and earthquakes, we obtained information regarding the date and time of blasting in the target period from the cement company in advance. Figure 2a shows the distribution of epicenters of the selected dataset. The selected earthquakes and quarry blasts have the same magnitude range (1.5 < Ml ≤ 3) (Fig. 2b). Figure 2c shows the number of earthquakes and quarry blasts recorded at each seismic station. Table S1 of the supplementary materials lists the original parameters of the selected events. We selected waveform data based on the conditions that (a) the seismic event was recorded at more than four stations (Fig. 2c), and (b) waveforms were unclipped with high signal-to-noise ratios (S/N). Figure 3a and b show an example of the recorded data for an earthquake on October 22, 2017, and for quarry blasts on February 11, 2018, respectively. The waveforms of the observed quarry blast show trailing phases in addition to the initial P-waves, which makes the discrimination difficult. Waveform data selected are pre-processed using seismic analysis code software (Goldstein and Snoke 2005).

Fig. 2
figure 2

A Epicentral locations of the 302 earthquakes (green circles) and 112 quarry blasts (red stars) used in this study. Histograms of B the number of events in each magnitude bin and C the number of events recorded at each station used in this study

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Fig. 3
figure 3

Examples of recorded waveforms for A the earthquake occurred on October 22, 2017, and B the quarry blast conducted on February 11, 2018. The local magnitude (Ml) of both events was 2.2

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2.1 Spectral decomposition method

The waveforms of shallow earthquakes and quarry blasts to derive the P-wave source spectra were extracted and the displacement spectra over a 1.28-s window for noise and signal before and after the P-wave arrivals and ensured acceptable signal to noise ratio (S/N) for all spectra used in the analysis was computed (see an example in Fig. 4). The signal to noise amplitude ratio exceeds 10 in the frequency range between 0.8 and 50 Hz for all events are used in this study. A Parzen window of 5.0-Hz duration was applied to smooth the obtained spectra. The obtained P-wave displacement spectra are a superposition of the different effects and responses (source, path, and site effects),

$${O}_{ij }\left(f\right)={S}_{i}\left(f\right){G}_{j}\left(f\right){P}_{ij}\left(f\right)$$
(1)

where Oij (f) is the observed spectra from source i at receiver j, Si (f) is the source effect, Gj (f) is the site response, and Pij (f) represents seismic wave attenuation along the path. The next step is to process the spectra to remove the path and site effects from the observed spectra. Equation (1) is a linear system of equations with an overdetermined problem, resulting in a trade-off between the source and site results. This shortage is solved numerically using a robust iterative least square analysis using a reference site.

Fig. 4
figure 4

Examples of A the calculated signal to noise ratio for P-wave spectra in m·s (i.e., meter. second) at each station for the earthquake occurred on October 22, 2017, and B those for the quarry blast occurred on February 11, 2018

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The reference site is a rigid rock site with no amplification compared to other receiver sites. However, many studies in other regions pointed out that finding such a site is difficult (Steidl et al. 1996; Archuleta 1998). Other researchers assumed that the amplification at the possible reference station site is around 2.0 due to the free surface effect (e.g., Iwata and Irikura 1988; Takemura et al. 1991; Kato et al. 1992). The significant disadvantage of referencing a rock site is that the number of processed events is limited to the number of events recorded by the selected reference site. Saadalla et al. (2020, 2019) pointed out that station New Gabal Marwa (NGMR), which belongs to the Aswan seismic sub-network, has a uniform amplification curve around 2.0. The station can be used as a suitable reference site in the inversion analysis.

The data selection procedure, data preparation, and spectral inversion analysis assume reference sites are applied with the same weights, conditions, and constraints for both families of earthquakes and quarry blasts of the dataset. The spectral analysis follows Andrews (1986) and Iwata and Irikura (1988). The inversion finally yields the site terms except for the reference site and anelastic attenuation factor of the area. These factors are introduced into Eq. (1) to obtain the source spectra for all events at all observation sites. We regard the average source spectra of all available stations derived for each event as the source spectrum for the individual event.

2.2 Source parameter estimations

We obtained theoretical P-wave source spectra for earthquakes and quarry blast based on the omega-square source formula proposed by Brune (1970). Brune’s model relates seismic moment to the displacement source spectra as

$${M}_{0= }\frac{4\pi \rho {v}^{3}}{F \times {R}_{\theta \varphi } \times G(r,h)}{\Omega }_{0}$$
(2)

The seismic moment M0 is calculated from the flat-level P-wave displacement spectra Ω0. Here, ρ is the density, v is the P-wave velocity at the source, F is the free surface effect, Rθφ is the P-wave radiation pattern, and G(r,h) is the geometrical spreading effect. Radiation patterns were used for correction using 0.52 for the P-wave (Aki and Richards 2002). We obtained values of ρ and v from the 1D seismic velocity model of Khalil et al. (2004) (Table 1). The corner frequency fc of each event is estimated by finding the intersection between the low- and high-frequency asymptotes of the obtained displacement source spectra (Baumbach and Borman 2012; Kiratzi and Louvari 2001; Ashkpour Motolagh and Mostafazadeh 2008; Havskov and Ottemoeller 2010). Andrews (1986) proposed a method to determine fc from the flat displacement spectrum edis (f) at the low frequency and the flat level of the acceleration spectra eacc (f) at the high frequency as described in Eqs. (3), (4), and (5).

$${e}_{dis}\left(f\right)=\frac{{\Omega }_{0}}{1+({f/{f}_{c})}^{2}}$$
(3)
$${e}_{acc}\left(f\right)=\frac{{\omega }^{2}{\Omega }_{0}}{1+({f/{f}_{c})}^{2}}= \frac{{(2\pi {f}_{c})}^{2}{\Omega }_{0} }{{({f}_{c}/f)}^{2}+1}=\frac{{A}_{0}}{{({f}_{c}/f)}^{2}+1}$$
(4)
$${{f}_{c}}_{ }=\frac{1}{2 \pi } \sqrt{\frac{{A}_{0}}{{\Omega }_{0}}}$$
(5)

where A0 is the flat level of the acceleration spectra. The obtained seismic moment for each event is used to calculate the moment magnitude using the formula of Kanamori (1977),

Table 1 Velocity model after Khalil et al. (2004) for the Aswan region
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$${M}_{W}=\frac{2}{3}{log}_{10}{M}_{0}-10.7$$
(6)

3 Results and discussions

Figure 5a shows an example of the average source spectra and standard deviation of the earthquake that occurred on October 22, 2017, at 11:20 (GMT), and Fig. 5b shows an example of the average source spectra of the explosion on February 11, 2018, at 06:32 (GMT) with the standard deviations. The obtained spectra slightly vary among observation points, implying that the inversion errors and scattering effects caused by different azimuths of the receivers are small. We calculated corner frequencies and seismic moment values for each event based on Eqs. 3–6 to discriminate between natural earthquakes and artificial events. Figure 6a and b show that the obtained displacement source spectra for quarry blasts show steeper falloffs at high frequencies in comparison with earthquakes. We found that the displacement P-wave source spectra of the earthquakes show suitable fittings with the omega-square models, where the quarry blasts indicate large misfits. Figure 7a and b show the fitting results with the omega-square models for different-size earthquakes and quarry blasts, respectively. Additionally, the quarry blasts exhibited steeper falloffs at high frequencies than earthquakes, which led to lower stress drops and corner frequencies. The deficiency of quarry blasts from high-frequency seismic energy was attributed to the ripple firing mechanism of the quarry explosions (e.g., Allmann et al. 2008; Shearer and Allmann 2007; Su et al. 1991; Bennett and Murphy 1986; Taylor et al. 1988).

Fig. 5
figure 5

A Estimated P-wave source spectra at each station (thin colored curves), their average spectrum (thick black curve), and the standard deviation at each frequency (blue vertical bars) for the earthquake occurred on October 22, 2017, and B those for the quarry blast occurred on February 11, 2018

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Fig. 6
figure 6

Derived displacement P-wave source spectra for A 302 earthquakes and B 112 quarry blasts

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Fig. 7
figure 7

Comparisons between the derived source spectra (colored solid lines) and the omega-square source model (black dashed lines) for different magnitude bins of A earthquakes and B quarry blasts

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Figure 8 shows the corner frequencies of the earthquakes and quarrying of the same magnitude ranged between 8.8–23 Hz and 1.0–2.6 Hz, respectively. We obtained the seismic moment values are in the range 2.03 × 1011–1.35 × 1012 Nm for quarry blasts and 1.02 × 1010 to 5.83 × 1012 for natural earthquakes, respectively. Quarry blasting has a narrower seismic moment range than shallow earthquakes in the Aswan region (Table S2, supplementary material).

Fig. 8
figure 8

A Calculated moment magnitude and estimated corner frequencies for earthquakes (blue circles), and quarry blasts (red stars). B Histogram distribution for earthquakes (blue column), and C for quarry blasts (red column)

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We finally discuss the discrimination method between earthquakes and quarry blasts. The classification between explosion events and earthquakes is usually done in a simple manner based on the epicenter location, epicenter depth, or the complexity of the waveform. However, the locations and depths between quarry blasts and earthquakes are similar, and the waveforms-based classification is not easy because of the presence of later phases in both waveforms (Fig. 3). We attempted to identify earthquakes and explosions using different magnitude scales; Ml was obtained from the earthquake catalog, and Mw was derived from this study. Figure 9 shows the relationship between Ml and Mw for both earthquakes and quarry blasts. The relationship derived from earthquakes is Mw = 0.7 Ml + 0.4, while Mw = 0.6 Ml + 0.9 for quarry blasts, indicating that the discrimination using the Mw- Ml relationship is not realistic. Our results suggest that only the spectral properties and source parameters could be used as reliable and robust discriminant factors. The most applicable method is deriving the source spectrum from the observed P-wave by removing the path and site characteristics obtained in this study and evaluate the agreement with the omega-square model. Alternatively, using the ratio of the amplitudes in the low-frequency component (< 10 Hz) to those in the high-frequency component (> 20 Hz) from the P-wave spectra of the observed waveforms as an indicator may enable the identification.

Fig. 9
figure 9

Estimated relationship between local magnitudes from the catalogues (Ml) and obtained moment magnitudes (Mw) for A earthquakes and B quarry blasts

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4 Conclusions

A total of 3069 seismic records were analyzed from 112 quarry blasts and 301 earthquakes with local magnitudes (Ml) between 1.7 and 3.1 from 2010 to 2021. The obtained P-wave displacement spectra from local shallow earthquakes and quarry blasts revealed various spectral properties and parameters (corner frequency and seismic moment) between both types of events. The Fourier spectra of quarry blasts poorly fit the omega-square source model and have lower fc than shallow earthquakes. The P-spectra for the quarry blasts have steeper falloffs and attenuated rapidly at a lower frequency in comparison with shallow earthquakes in the same tectonic region and within the same magnitude range. The investigated quarry blasts had longer source time durations of blasts than natural earthquakes, leading to lower corner frequencies and low-stress drop values than the studied natural seismic events. Our study focused on Aswan shallow earthquakes and quarry blasts, but we expect the obtained discriminant parameters and insights found in this analysis will be applicable to other regions of more direct interest to mining and quarrying. The spectral properties obtained in this study are helpful in event discrimination processing. Making a catalog of quarry blasting will be essential to evaluate the hazards and environmental impacts of the mining development in Egypt.