Citation: Adam, H. A., T. Hayashida (2023), Estimation of crustal structure in northern Egypt using
broadband seismic Ambient noise (5-50 s), Synopsis of IISEE-GRIPS Master’s Thesis.

ESTIMATION OF CRUSTAL STRUCTURE INNORTHERN EGYPT USING BROADBAND SEISMICAMBIENT NOISE (5–50 s)
Hany Abuelnaga ADAM1
MEE22701

Supervisor: Takumi HAYASHIDA2

ABSTRACTThis study presents a comprehensive group velocity map for the crustal structure estimation in NorthernEgypt using ambient noise tomography (ANT) focusing on periods from 5 to 50 s. The study area islocated in Northern Egypt and encompasses regions bounded by the Red Sea to the east and theMediterranean Sea. We collected seismic ambient noise data from 24 broadband seismic stations fromJuly 2021 to June 2022, with an interstation spacing ranging from 50 to 1000 km. Green’s Functions
were derived from the cross-correlation of the recorded data, resulting in 276 station pairs for analysis.
Accordingly, 100 to 250 reliable measurements were selected based on the signal-to-noise ratio and thestandard deviation of seasonal dispersion curves. Utilizing the dense coverage of the ray paths enabled
the generation of group velocity maps with 5 to 30 s periods, offering spatial resolutions between 50 and
400 km. The derived map revealed two distinct structural zones: a high-velocity zone in the eastern partand a low-seismic velocity zone in the western region. To further validate the results, one-dimensional
shear wave velocity profiles were inverted from the observed group velocities. We found that theestimated Moho depths range from 40 km in the southwestern part to 35 km in the north and easternparts. Our results show that ANT is a powerful and advantageous approach, particularly in nonactive
seismic areas, effectively overcoming the challenges encountered in conventional receiver function
analysis and seismic tomography.
Keywords: Seismic Ambient Noise, Rayleigh Wave, Group Velocity, Northern Egypt.
1. INTRODUCTION
Egypt is located in the northeastern corner of the African Plate, bordered by the eastern Mediterranean
Sea to the north and the Red Sea to the east. The tectonic setting of the region is characterized by bothdivergent and convergent plate boundaries, interacting with the Arabian Plate and the Eurasian Plate,respectively. This convergence has resulted in the formation of significant geological features, such as
the Hellenic Arc and the Cyprus Arc, which are subduction zones where the African Plate is undergoing
downward displacement beneath the Eurasian Plate. The principal tectonic components influencing thegeological landscape of Egypt are the African-Eurasian Plate margin, the Red Sea plate margin, and theAqaba-Dead Sea transform fault (Collins et al., 2021). This research represents the initial effort to obtainthe velocity structure of the crust and upper mantle in Egypt using Ambient Noise Tomography (ANT).
Examining the crustal structure and a comprehensive understanding of geodynamic processes in this1
2

National Research Institute of Astronomy and Geophysics (NRIAG), Egypt.
International Institute of Seismology and Earthquake Engineering, Building Research Institute (BRI).

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area are important as they contribute to seismic hazard assessment and risk mitigation in Egypt. By
shedding light on the subsurface characteristics and seismic properties, this study aims to provide
valuable insights into the geological complexities of the region, potentially aiding in the developmentof more robust strategies for disaster preparedness and mitigation in the area.
2. DATAWe utilized one year of continuous record ofambient noise data (July 2021 to June 2022)
from 24 broadband seismic stations within
the Egyptian National Seismological
Network and the GeoForschungsZentrum(GFZ)
network.
The
geographicaldistribution of these stations covers a rangeof station spacing from 20 to 1000 km. The
compiled dataset undergoes testing using thepower spectral density (PSD) technique, as
introduced by Peterson (1993). PSD serves
as a valuable tool for assessing instrumental
anomalies,
geographicaldisparities,
proximity to local noise sources, and diurnaland seasonal fluctuations.

Figure 1. A tomographic map of the study area. Red
triangles refer to the distribution of 24 broadband
seismic stations used in this study.

3. METHODOLOGY AND RESULTS
Bensen et al. (2007) introduced a comprehensive framework for processing ambient noise data with a
primary focus on its application in surface wave tomography, specifically for Rayleigh waves. The
proposed approach comprised four distinct phases: (Phase 1) preprocessing of data obtained fromindividual stations; (Phase 2) cross-correlation (CC) and stacking procedures essential for enhancing thesignal-to-noise ratio; (Phase 3) extraction of the observed dispersion curves that are crucial for furtheranalysis; and (Phase 4) tomographic inversion for subsurface imaging.
3.1. Phase 1: Single Station Data Preparation
After collecting of the vertical-component ambient noise data from 24 stations and testing the quality,
we selected the data based on the daily waveform of more than 80%. The data gap between traces mustbe less than 25 s and filled with zero amplitude. First, the instrument response was removed to eliminateany instrumental artifact. Next, the data underwent demeaning, detrending, and tapering operations toremove any mean offsets, long-term trends, and potential spurious signals that may interfere with theanalysis. Subsequently, the data were filtered on a 5 to 50 s period range and downsampled from theinitial 100 SPS to 1 SPS. Temporal normalization techniques were employed in the time domain to
mitigate the influence of the local earthquake signals and local seismicity on the data. Then spectral
whitening is applied in the frequency domain to remove the secondary microseismic band from the data.
3.2. Phase 2: Cross-Correlation and Time Stacking
ANT utilizes CC to multiply the signals of two stations and integrate them over a time series period,
deriving a time delay, called Green’s Function. The cross-correlation function (CCF) is computed dailyusing the raw data for each station pair. These daily, monthly, and yearly results are stacked to yield alonger time series, which enhances the SNR. The recorded signals from both receivers were thensubjected to a CC analysis that provides the CCF (CAB(τ)) of ambient noise uA(t) and uB(t) at stations
A and B, respectively.

2𝐶𝐴𝐵 (𝜏) = ∫ 𝑢𝐴(𝑡)𝒖𝐵 (𝑡 + 𝜏)𝑑𝑡

(1)

𝑑𝐶

Distance (km)where, τ represents the time shift or delay time.
The [N (N − 1) / 2] formula was utilized todetermine the total number of possible station
pairs, where N represents the number of seismicstations used. Our dataset resulted inapproximately 276 station pairs, from which weobtained the CCFs. The CCF results were stored as
two-sided time functions encompassing positive
and negative time lags. The time lags represent thetime delay between the signals proportional to theseparation distance between the two stations andthe Rayleigh wave velocity of the medium. The
results of CC are recorded in positive and negative
time lag shown in Figure 2, which represents thewaves traveling between the station pairs in
opposite directions (Bensen et al., 2007). The GF
was computed as the time derivative of the averageof the positive time lag signal (𝐶_𝐴𝐵(𝑡)) and its
corresponding negative time lag signal(𝐶_(𝐴𝐵)(−𝑡)) (𝐸𝑞. (2)):

Time lag (s)Figure 2. Record section derived from 12 monthof ambient noise cross-correlations (CCFs) for
XX station pairs (vertical component). The CCFs
were filtered between 5–50 s and normalized byabsolute mean normalization and plotted as afunction of interstation distance.

𝐺𝐴𝐵 (𝑡) = − 𝑑𝑡 [ 𝐴𝐵(𝑡)+ 𝐶𝐴𝐵 (−𝑡)2](2)Note that C_AB(t) and C_(AB)(-t) represent signals with positive and negative time lags, respectively.
Obtained Signal to Noise Ratio (SNR)SNR

SNR is defined as the ratio of the maximum
amplitude of the signal window to the root meansquare of the noise window. SNR is a crucial
parameter indicating the CCF’s quality andassisting in selecting reliable period bands fordispersion measurements. In this work, the SNR
was calculated by first defining the signal window
size using the minimum and maximum grouparrival times (3.5/distance < t < 2.5/distance) andsetting the noise window size to 500 s, starting
after the trailing noise ends, that is, the end of thePeriod (s)signal window (Bensen et al., 2007). Several
factors influence the SNR value, including theFigure 3. Four examples of spectral SNR for afollowing:
CCF between two stations pairs (KAT-RASM)
1- Time series length: The SNR generallywith station spacing 80 km, at different stacking
increases with longer time series lengths
lengths (1, 3, 6 and 12 months).
(Yang et al., 2008). Hence, the more extended
time series of the CCF yields improved SNRvalues (Figure 3).
2- Local noise conditions: The prevailing noise environment affects the SNR.
3- Interstation distance (path length): The closer station pairs show higher SNR values, especially inshort periods.

33.3. Phase 3. Obtained Surface Wave Dispersion Curve
The dispersion curve was constructed by applying frequency-time analysis (FTAN) and extracting thegroup velocities at different frequencies (see an example in Figure 4). It depicts the relationship between
the group velocity and the period for the surface waves propagating through the region of interest, asdescribed in previous studies (Levshin & Ritzwoller, 2001; Bensen et al., 2007). Analysis of the 276
dispersion curves obtained from the 24 seismic stations revealed distinct group velocity variations acrossthe interested area. We observed that the station pairs in the western part showed notably lower groupvelocities at shorter periods. In contrast, the station pairs located in the eastern part of the study area
showed higher group velocities at shorter periods, with a less pronounced increase achieved as theperiods lengthened.

Figure 4. Examples of frequency-time analysis (FTAN) calculated from 12 months of stacked CCF for
one station pair, (GE.EIL-HL.NAT). Left panel: symmetric component of the original CCF and set ofGaussian band-pass filter, signal window marked by cyan color and noise window marked by gray color,
SNR and period bands are centered at each panel. Two middle panels: raw and clean FTAN andcorresponding observed dispersion curves. The green vertical line represents the cut-off period, and error
bars on the blue line indicate the standard deviation. Right panel: location of station pairs on the map of
the study area at the top and average SNR at different periods at the bottom.
3.4. Phase 4: Tomographic Inversion Map
A tomographic inversion is a group velocity map generated by inverting the observed dispersion curves
at selected periods. The inversion process is typically executed through a forward modeling algorithm
that simulates the seismic wave propagation within a given subsurface model. The theoretical dispersion
curves are compared with the observed data, and the discrepancies between the two are utilized to update
the model parameters. This iterative process continues until the differences between the observed andsynthetic data are minimized, culminating in the final and refined model of the subsurface. As outlinedby Barmin et al. (2001), the tomographic method’s algorithm relies on a relationship at any period T
described:

𝑑 = 𝐺𝑀 + 𝜎(3)where the data vector (d) represents the differences observed in the reference travel time (travel timeperturbations) between the station pairs deduced from the measured group velocities at period T. The
data model (𝑀) comprises the best-fitting parameters (𝑣0 − 𝜈)/𝜈 at the grid nodes, representing
slowness perturbations along the regular grid nodes. ν denotes the observed velocity derived from thedispersion curve, while 𝑣0 represents the reference velocity (inverse of the mean slowness). The
slowness implied by the observed travel times is calculated as the sum of the observed travel times

4divided by the sum of the interstation distances. The forward matrix (G) is a coefficient that facilitatesslowness integration along the wave paths. σ denotes the standard deviation for 𝑑.

Figure 5. Examples of Rayleigh wave group velocity map at periods 10 s. Left panel: path densities
for each grid node. Middle panel: group velocity distribution. White-colored areas are those without ray
path coverage. Right-panel: spatial resolution map from 50 to 500 km. White triangles refer to theseismic stations used in this study.
4- ONE DIMENSIONAL SHEAR WAVE VELOCITY MODEL
After obtaining the observed dispersion curve at any selected point from the group velocity map, weinverted the information to shear wave velocity with depth. The inverse problem of determining theinput data from the given output data was addressed by using the Markov Chain Monte Carlo (MCMC)
method employing the Metropolis algorithm (Mosegaard & Tarantola, 1995). The misfit value of thecalculated dispersion curve to the observed dispersion curve is derived as follows:
𝑁𝑆(𝑚) = ∑(𝑣𝑔𝑚 (𝑇𝑖 )− 𝑣𝑔𝑜𝑏𝑠 (𝑇𝑖 ))2

𝑖=1(4)

2𝜎 2 (𝑇𝑖 )where, 𝑣𝑔𝑚 and 𝑣𝑔𝑜𝑏𝑠 𝜎 represent the calculated andobserved dispersion curves, respectively, and σ denotes thestandard deviation of the observed dispersion curve at period
𝑇𝑖. When we compare the results of the Moho depth fromthis study by using ANT and previous work from the joint
inversion of receiver function and surface wave dispersion
method (Hosny & Nyblade, 2016), we can see no big
differences at the same points, for example at FRF and KOTstations located at Eastern Desert and Western Desert
respectively, the Moho discontinuity located at 41.4 ± 2.37
km at FRF and 36 ± 2.0 km at KOT stations, that coincidewith the previous results, while the Moho discontinuity is
located at 35 and 38 km at FRF and KOT respectively.
Figure 6. Ensemble models of one-dimensional inversion ofgroup velocity dispersion curve at point C in theNorthwestern Desert. The black line in the upper figure
indicates the observed dispersion curve, error bars are the stander deviations around the group velocities.
The gray color is represented 95 % of the 5000-confidence sampled interval of theoretical groupvelocities function of the period (upper) and corresponding shear velocity function of depth (lower).
Theoretical group velocities function of the period (upper) inverted to initial models (lower). Black5

dotted lines mean confidence sampled dispersion curves (upper), and the mean of corresponding models(lower). Red lines in the upper and middle figures are the best-fitting models.
5. CONCLUSIONSThis study aimed to investigate the crustal structure of the upper and lower crust in Northern Egypt
Continuous seismic data collected over a 12-month period from 24 vertical component broadband
permanent seismic stations within the region were used in this work. The ANT data processing
comprised four essential steps: 1- preprocessing, 2- Cross-correlation analysis for 276 station pairs toderive Green’s Functions and stacking the results for one year, 3- Obtaining Rayleigh-wave groupvelocity dispersion curve by applying FTAN to the time lag of the CC results for each station pair. 4Construction of group velocity map for periods ranging from 5 to 30 s. Our study area can be delineatedinto distinct zones characterized by varying velocities based on the group velocity map analysis. Theeastern region encompassing the Sinai Peninsula and Northeastern Desert exhibits high-velocity values,
while the Northwestern Desert constitutes a low-velocity zone. The Nile River acts as a natural boundary
demarcating these two primary regions. Velocity anomalies were also observed, with certain areas in
the eastern high-velocity zone displaying lower velocities, and vice versa, in the western low-velocityzone. The Metropolis algorithm method was employed to develop one-dimensional shear wave velocity
models at each grid point. The estimated Moho depths are between 40 km in the Southwestern Desert
to 35 km in the Northeastern Desert and Sinai Peninsula. This approach enables the exploration of
possible velocity models and the identification of the best-fitting model for the subsurface structure.
ACKNOWLEDGEMENTS
I would like to express my heartfelt gratitude and appreciation to everyone who has contributed to myacademic journey and enriched my experience during this course. First and foremost, I extend mydeepest thanks to my supervisor, Dr. Takumi Hayashida, for his unwavering support and guidance
throughout this course and my master's work. I would also like to acknowledge Dr. Tatsuhiko Hara, thecourse leader, for his valuable support, insightful comments, and helpful advice during our individual
study. Additionally, I extend my thanks to all the esteemed staff members at IISEE/BRI whose expertiseand teachings have contributed significantly to my learning experience. It fills me with pride to havelearned from such knowledgeable individuals.
REFERENCES
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