Bekhit et al.
Progress in Earth and Planetary Science(2024) 11:39
https://doi.org/10.1186/s40645-024-00639-6

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Crustal thickness variations beneath Egypt
through gravity inversion and forward
modeling: linking surface thermal anomalies
and Moho topography
Ahmed Mohamed Bekhit1*, Mohamed Sobh2,3, Mohamed Abdel Zaher2, Tharwat Abdel Fattah1 andAhmed I. Diab1

Abstract
This study aims to quantify the topography of the Moho boundary, the lower crust and uppermost mantle contact of Egypt, in order to estimate the crustal thickness variation and its link to the distribution of thermal anomalies under Egypt. This is accomplished by modeling satellite gravity, supported by the passive seismic constraints
throughout Egypt. However, when estimating the thickness of the crust in Egypt using just seismic data, substantial
uncertainty and deviation are produced due to the sparsely dispersed stations. Integrating seismic and gravity data
minimizes uncertainty and improves estimate accuracy. The investigation is broken down into four stages, the first
involving utilizing the Sentinel-3B satellite to create land surface temperature maps. The subsequent steps consistof gravity and seismic data adjustments, inversion and forward modeling. We used seismically restricted nonlinear inversion to look at Goco06s satellite gravity data to model the Moho’s topographic surface. The data gathered from deep seismic refraction and receiver functions adjusted the analyzed data. The inversion process relieson the adapted Bott’s approach and Tikhonov regularization, using the assumption of the sphericity of the Earthplanet. Reference values for depth of Moho and density contrast were set at 35 km and 500 kg/m3, respectively. The
average statistical difference for Moho depth between gravity-based model and seismic data is − 0.10 km. Throughforward gravity modeling, five gravity profiles were chosen and interpreted in 2.5D models. The results indicatedthat the Moho depth in the south varies from 35 to 39 km and decreases in the north and the Mediterranean. In
upper Egypt, the highest Moho depth is 39 km. The depth varies beneath the Sinai Peninsula as it is about 35 km in its
south, reaches 30 km in the northern portion, and ranges along the Red Sea’s Rift Margin from 29 to 32 km. Moreover, the final model shows the relation between Moho coincides with the surface temperature anomalies approvedby satellite images and hot springs. The model reveals a correlation between Moho discontinuity and surface temperature anomalies, revealing the highest geothermal potential in a rectangular area in central Egypt, between latitudes
25°N and 30°N, based on satellite imagery and hot springs distribution.
Keywords Moho depth, Thermal anomalies, Satellite gravity, Inversion model, Forward modeling*Correspondence:
Ahmed Mohamed Bekhit
ahmed.m.bekhit@alexu.edu.eg
1Geology Department, Faculty of Science, Alexandria University,
Alexandria, Egypt
2
National Research Institute of Astronomy and Geophysics (NRIAG),
Helwan 11421, Cairo, Egypt
3
Leibniz Institute for Applied Geophysics, Hannover, Germany© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

1 Introduction
The Moho is a clear boundary that marks the transition between the crust and the uppermost mantle. Thisboundary is particularly important as it marks a significant shift in rheology, velocity of seismic and chemicalcomposition, also characterized by a significant change
in elastic properties, which is caused by a substantial
difference in the types of rocks present (Abdelwahed
et al. 2013). By mapping the depths of the Moho, we canacquire information about the thickness of the crust, theway continental plates are balanced in terms of weight,
and the distribution of abnormal heat patterns.
Gravity is effective in Moho boundary geometry studies because of the strong-density contrast on this interface between the mantle and the crust from 2900 kg ­m−3
to 3300 kg ­m−3, about 400 kg ­m−3 density contrast (Gaber
et al. 2022). One of the essential issues to model the entire
earth heat flow and investigating the total hydrocarbon
elements is to understand accuracy the deep structure(Bouman et al. 2015; Hantschel and Kauerauf 2009). In
this study, the Moho depth and crustal thickness will bemodeled to get better understand for the earth’s interior,beneath Egypt, using an integration approach of seismic
and gravity data (Fig. 1).
Egypt can be divided into four units based on its geomorphology (Rushdi 1990): western desert, cultivatedland, eastern desert and Sinai Peninsula (Fig. 1). The

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dynamics of Egypt’s lithosphere are governed by the driftof three major plates: Africa, Arabia and Eurasia, generating a complicated geodynamic setting (Saleh et al. 2006).
Multiple studies investigated Egypt’s Moho depth, gravity, seismic refraction, deep sounding, receiver function,
seismological time inversion, Rayleigh wave and spectral ratio technique. (Sobh et al. 2019) estimated crustal
thickness in Egypt using inverse and 3D forward gravity
modeling, concluding it ranged from 25 to 45 km. (Gaber
et al. 2022) evaluated the crustal thickness and structural
patterns of the Sinai Peninsula using 3D density modeling of aeromagnetic and earthquake data. (Abdelwahed
et al. 2013) developed 3D structural models of the Conrad and Moho discontinuities across Egypt’s different
tectonic domains. This paper deals with investigationof the Mohorovii (Moho) discontinuity characteristics
using seismically constrained inversion of satellite gravity
data. Our mission is to identify higher geothermal potential zones in Egypt based on crustal thinning and Mohonearer the earth’s surface, confirming this with locationof hot springs (Zaher et al. 2018) (Table 1) and high land
surface temperatures. This study serves as essential for
future geothermal investigation in Egypt. Several models:
the global relief (Etopo1) model, the (Crust1) and manyothers, along with earlier seismic research, provide constraints on the geometry and physical parameters that
affect the gravity inversion.

Fig. 1 Egypt’s shaded digital elevation model developed from the etopo1 (Amante 2009), showing the location of seismology stations, cyan points
indicating to the seismology stations (Hosny and Nyblade 2016), red points indicating to the hot springs (Zaher et al. 2018)

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

2 Geological setting
Egypt is situated at the border between Africa andAsia, with a geographically square territory of around
1,000,000 ­km2. The Mediterranean Sea forms the northern boundary, and the Red Sea lies at the eastern border.
Egypt is flanked by three active tectonic plate boundaries:
the Afro-Eurasian boundary, the Red Sea boundary, and
the Aqaba-Dead Sea fault, as shown in Fig. 2. As a result,
Egypt undergoes complex and essential tectonic interactions that encompass compressional, extensional andshear movements. Egypt has seven primary geographical areas: (1) the Nile valley and delta, (2) El Fayum, (3)the Suez Canal, (4) the Western Desert, (5) the Eastern
Desert, (6) the Sinai Peninsula and (7) the islands in the
Red Sea (Ball 1939).
Recent global positioning system observations reveal
Africa’s northwestward movement relative to Eurasia at a
rate of 6 mm per year. The Red Sea’s spreading rates range
from 14 mm per year to 5.6 mm per year. The southern
Aqaba-Dead Sea fault experiences left-lateral strike-slip
motion. These tectonic events cause secondary deformation and insignificant earthquake activity (McClusky
et al. 2000 and 2003; Wdowinski et al. 2004).
Geophysical investigations conducted in Egypt haverevealed three significant tectonic trends: the Mediterranean trend (E-W), the Red Sea trend (NW–SE) and the
Gulf of Aqaba trend (NE-SW) (Yousef 1968; Saleh et al.
2024). Tectonic forces are very complicated, so mostof Egypt’s seismic activity is limited to four areas: theLevant-Aqaba band, the Northern Red Sea-Gulf of SuezCairo-Alexandria region, the eastern MediterraneanPage 3 of 15

Cairo-Fayum zone and the Mediterranean Coastal Dislocation Belt (Kebeasy 1990; Abou Elenean 1997).
Egypt has stable and unstable shelfs, separated by the
Hinge zone. The stable shelf features moderate tectonic
deformations and sedimentary layers, with a narrow
sequence near the Arabian-Nubian Shield (Said 1962).
The Hinge Zone, which lies between the unstable shelfand the miogeosynclinal basinal region, shows a significant rise in sediment thickness from the Oligocene to
the Pliocene period. Significantly, this zone correspondswith the modern Mediterranean shoreline region. However, certain granitic outcrops in southern Egypt havebeen recognized as a component of the extremely ancient
Saharan Metacraton. Figure 2 simply illustrates the Egyptian geological map and its major characteristics. The
Keraf shear zone divides the Neoproterozoic terrains
and the Metacraton from east to west, respectively; thePrecambrian Basement is overlain by Phanerozoic sedimentary strata spanning the majority of Egypt’s land. On
the opposite side, Paleozoic shales and sandstones represent the foundation of the sedimentary group, which isfrequently topped by Mesozoic sediments, followed by
Paleogene and Neogene sediments and topped by the

Table 1 Location and temperatures of hot springs in Egypt,obtained from (Zaher et al. 2018)
No

Name

Longitude

LatitudeTemperature(°C)

1

Ain-El-Arayes

25.61095

29.14931

27

2

Ain-Dalla

27.28179

27.23936

35

3

Ain-El-Balad

27.98223

27.04802

28

4

Ain-El-Gabal

28.26575

25.7769

40

5

Ain-El-Ris

28.65016

28.03271

28.3

6

Ain-El-Bishmo

28.88271

28.37361

31.6

7

Ain-Bowitti

28.91077

28.38708

33.7

8

Ain-El-Kasr (Qasr)

29.21058

25.56571

39

9

Ain-El-Bousa

30.55598

25.3605

31

10

Ain-El-Sira

31.24267

30.0035

40

11

Ain-Helwan

31.32366

29.86136

36

12

Ain-Sukhna

32.32966

29.62754

33

13

Ayun-Musa32.64627

29.79642

37

14

Hammam-Pharaun

32.95607

29.19656

71

15

Hamman-Musa33.59566

28.2655

48

Fig. 2 The Surface Geology map of Egypt modified after (Hosny et al.
2014); the geologic units are illustrated through a colored legend;
red solid lines delineate the tectonic boundaries. Red Sea Axial rift
and the recent major faults modified after (Abou Elenean 2007;
Abdelwahed 2013), and the black lines show the faults

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

unconsolidated Quaternary deposits (Abdelsalam et al.
1998; Abdelsalam and Stern, 1996).
The sedimentary cover in Egypt encompasses theentire Phanerozoic Eon, with representation from all geologic periods. As indicated in Egypt’s basic geologicalmap (Fig. 2), volcanics and Phanerozoic sediments spanaround 90% of the territory, while the basement constitutes
approximately 10% of the region. However, many earlier
sedimentary rocks, particularly Paleozoic formations, arehidden within very deep basins beneath more recent rockstrata. Underground investigation has greatly improved
our grasp of Egypt’s geological past. Phanerozoic volcanic
activities are geographically and temporally broad, invading both the basement and the sedimentary overlay. These
volcanic activities are related to crustal processes. Nonetheless, the country has frequently exhibited stable cratonic
behavior throughout the Phanerozoic Eon. Egypt’s geology
is split into four major rock groups: the Basement, Paleozoic, Mesozoic and Cenozoic. The Mediterranean Sea andits progenitor, the Tethys Ocean, gave particular consideration to the sedimentary cover and its geological history (ElShazly 1977).
Hot springs in Egypt are natural occurrences resulting
from geothermal activity and have a notable impact on the
country’s hydrothermal system (Bilim 2017; Lashin 2013).
Egypt’s geology, which stands out for having a complex
tectonic past and a wide variety of geological structures,is closely related to the occurrence of hot springs in the
country. Hot springs are predominantly located in regions
characterized by ongoing faulting and volcanic phenomena, such as the rifting of the Red Sea and the Gulf of Suez.
These places have tectonic regimes that are characterized
by extension. This creates fractures and fault networks thatallow geothermally heated water to move upward (Bektas
2013; Zaher et al. 2018). Additionally, areas with higher
permeability, such as cracked basement rocks that makeit simple for geothermal fluids to circulate, are frequently
associated with hot springs. The geological structures,such as fault systems and permeable rock units, serve as

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pathways for the movement of fluids from deep underground. Geothermal gradients and natural increases intemperature with depth heat these fluids. Geothermal
springs, which are distinctive for their high temperaturesand mineral-rich water, are the result of this phenomenon(Zhang et al. 2017). An analysis of the distribution of hot
springs in Egypt offers useful information on the geologicalprocesses, tectonic activity and geothermal resources of thecountry. This analysis helps us gain a deeper understandingof Egypt’s geodynamic evolution and its potential for sustainable energy use. Although the exact nature of the interaction between these two parameters is not yet completely
known, multiple studies have indicated a possible connection. The depth of the Moho has an important impact oncontrolling the thermal structure and heat movement in
the Earth’s crust, which can affect the accessibility of geothermal energy. Geological features related to the Moho
depth, like fault networks and porous rock units, can also
help geothermally heated fluids move, which leads to theformation of hot springs (Huang et al. 2002). By examining
the relationship between hot springs and Moho depth, we
can obtain useful knowledge on the characteristics, distribution and potential resources of geothermal systems.

3 Materials and methods
The primary data sources for this study were two gravity datasets and the Level-2 Land Surface Temperature(LST) product from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel-3B (Table 2).
3.1 Land surface temperature (LST) mapping

The Sea and Land Surface Temperature Radiometer(SLSTR) is a temperature radiometer that operates in
low Earth orbit at an altitude of 800–830 km. It uses
dual-view scanning to measure the temperature of boththe sea and land surfaces. The instrument is installed on
both the Sentinel-3A and Sentinel-3B satellites, which
are now in operation as part of the Copernicus program.
The SLSTR instrument on Sentinel-3 plays a major role

Table 2 Description of the used datasets
No

Used dataset

Description

Source1

Etopo1

Global topography relief model

Amante and Eakins (2009)2

Crust1

Global sediment thickness model

Laske et al. (2013)

3

The Sea and Land Surface Temperature Radiometer (SLSTR) From Sentinel-3B satellite

Land surface temperature radiometer(https://​www.​onda-​dias.​eu)

4

Hot Springs

Hot Springs locations and temperatures (Table 1)

Zaher et al. (2018)5

GOCO06S

Satellite Gravity

Ince et al. (2019)6

XGM2019e_2159

Combination of satellite and terrestrial gravity measurements

Zingerle et al. (2020)

7

Passive Seismic stations

Stations with known Moho depth values

Hosny and Nyblade (2016)

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

in this operational mission. The efficacy of the SLSTR’s
Land Surface Temperature (LST) algorithm is contingent
upon various elements, such as the biome type, the diurnal cycle, the fraction of vegetation cover and the viewing
zenith angle. Regarding daylight data, the technique has
been evaluated and shown to have 1.8 K resolution and
1.2 K sensitivity. This is attributable to substantial thermal fluctuations commonly observed across land surfaces (Pérez-Planells et al. 2021).
3.1.1 Used data

LST, land surface temperature product from the Level-2of the above-mentioned satellite, was used in this investigation to acquire land surface temperatures at a resolution of 1 km. The temperature measurements were later
used to calculate the amount of heat produced on thesurface. A total of forty-four images spanning from September 29, 2021, to September 30, 2022, were collectedfrom an open access source (https://​www.​onda-​dias.​eu),
specifically focusing on MISSIONS: Sentinel-3B, PRODUCT: SL-2-LST, LEVEL: L2 and INSTRUMENT: SLSTR. The collected images underwent processing andcompression, resulting in the generation of eight consolidated images for further analysis.
3.1.2 LST data processing

The data were processed using the Sentinel Applications
Platform’s snap software in three steps: First, the measure
was converted from kelvin to degrees Celsius using theformula (K°− 273.15), and then the data were projectedusing raster technology (GIS referenced). The impact ofclouds obscuring the image is then eliminated during themasking step by employing the function (confidence insummary clouds).
3.2 Gravity data
3.2.1 Used data

This study made use of two separate gravity databases. The initial dataset consists of unprocessed gravity data acquired by the GOCO06S spherical harmonicmodel, which solely relies on measurements from theGOCE mission (Kvas et al. 2019). The GOCO06S version, derived from the European Space Agency’s GOCE
effort (Ince et al. 2019), generates the Moho depth model,
providing accurate Knowledge about Earth’s interior
structure and identifying longer wavelength anomalies
associated with deeper crust and shallower mantle layers.
This work includes the XGM2019 integrated model,
which combines satellite and terrestrial gravity measurements for improved accuracy. It is particularly useful forforward modeling due to its superior accuracy at shorter
wavelengths, enhancing resolution of shallower formations like sediments and top crust (Zingerle et al. 2020).

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3.2.2 Gravity data processing

It is necessary to get rid of any unwanted signals and concentrate on the desired data before applying the Moho
inversion. The Bouguer anomaly is used in our study as
the key piece of information for this reason. To ensure
the accuracy of the results, a number of effects were
eliminated during the data processing stage. Tesseroids,
an open-source program that divides the research region
into spherically shaped prisms, was utilized for processing (Uieda et al. 2016; Farag et al. 2022); we provided
our updated version of the processing software within
the supplementary materials. Throughout the processing steps, the study area was expanded by five degrees inall directions to reduce edge effects (Szwillus et al. 2016).
The GOCO06 data underwent a series of processing
steps that included the following to clean up the dataset
and get it ready for Moho inversion analysis:
1. The calculation of gravity disturbance eliminates normal earth’s influence (γ) using the closed-form algorithm, revealing residual gravitational anomaly due
to terrain, sediment and elevation changes (Li and
Gotze 2001) (Fig. 3a).
2. The topography was corrected for gravity disturbanceusing the etopo1 model and crust1 model (Amante andEakins 2009; Laske et al. 2013). The correction assumed
uniform density distribution, with 2670 kg representing top continental crust and 1630 kg representing
ocean waters. The topographical impact (Fig. 3b) wasremoved to create the Bouguer map (Fig. 3c).
3. The Bouguer anomaly and Moho effect are distinguished by removing gravitational influence fromsedimentary layers (Fig. 3d). The gravity impact isestimated independently using three sediment layerswith distinct densities (Laske et al. 2013).
4. Gravity correction assumes irregular Moho clearance isthe sole remaining component (Rathnayake et al. 2021;
Tenzer and Chen 2019) all corroborate this notion.
(Farag et al. 2022) described the entire Bouguer gravity
disturbance mathematically as:g B = g dis − g T − g T .δρ − g B − g MS − g IS − g C(1)where g B = the gravity anomaly without effect of sediments,
g dis = the gravity disturbance,g T = the gravitational effect of topography,
g T .δρ = the abnormal − topographic correction,andg MS = the marine sediment ,g B = the bathymetric,ISg = the inland sediment,g C = gravity of the solidified crust . (Fig. 3e) depicts the
final processed Bouguer map.

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

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Fig. 3 a Free air anomaly map of Egypt; b the gravitational effect of topography of Egypt; c the Bouguer anomaly of Egypt; d the gravity correctionof Egypt’s sedimentary cover; e final Bouguer map of Egypt; with a resolution of 0.1 arc degrees

3.3 Gravity inversion and Moho depth estimation

To characterize the architecture of the crust and figureout Moho depth, both an inversion technique and a 2D
forward density modeling approach were employed.
The Moho depth was initially estimated through theinversion of satellite gravity data obtained from the
GOCO06 dataset. Subsequently, the obtained results
were compared with five profiles derived from forward
gravity modeling using the combined gravity model
XGM2019e_2159.
To characterize the architecture of the crust andfigure out Moho depth, both an inversion techniqueand a 2D forward density modeling approach were
employed. The Moho depth was initially estimatedthrough the inversion of satellite gravity data obtained
from the GOCO06 dataset. After that, the results werecompared with five profiles that were made using the
combined gravity model XGM2019e_2159 and forward gravity modeling.
This study employs a nonlinear inversion methodwith gravity and seismic data, utilizing the Gauss–Newton formula and tesseroids to address the ill-posednessof the inverse problem (Sobh et al. 2019; Uieda andBarbosa 2017); we provided our modified version of
the inversion code in the supplementary materials. The
technique also incorporates Earth curvature and uses a
Tikhonov regularization factor (Farag et al. 2022). The
inversion technique determines three critical parameters: regularization parameter, Moho discontinuity reference depth (Zref) and crust-mantle density difference(Δρ). It involves calculating the regularization value
and performing inversion for different density contrast
and reference depth values.
To ensure robust comparisons and obtain reliablepredictions, the inversion process was constrained byseismic data, as indicated by the distribution of seismic
points shown in Fig. 1. A variety of values were tested,with increments of + 50 kg/m3 for the density disparity(ranged from 200 to 500 kg/m3) and + 2.5 km for thereference Moho depth (ranged from 10 to 50 km). The
final model was determined based on the best fit to theseismic data points. Subsequently, the inversion step
was performed for various combinations of density
contrast and reference depth values.

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

3.4 2D forward modeling with GM‑SYS

To complement our inversion model, a gravity modelingwhich is a 2D forward technique was carried out utilizing
the GM-SYS version of the Oasis Montaj software. We
assigned the following forward density values: 2400 kg/
m3, 2700 kg/m3, 2900 kg/m3, 3300 kg/m3 at sediment,
upper crust, lower crust and upper mantle, respectively(Makris and Wang 1994; Ginzburg and Ben-Avraham
1987). The objective of this modeling approach was tofurther investigate the subsurface structure and provide
supplementary evidence for our study. The 2D modelingprocess encompassed five distinct profiles (P1 to P5)
strategically positioned across different regions within
the study area. These profiles played an essential role incharacterizing the geometries of significant undergroundboundaries, including the Basement interface, the Conrad interface and the Moho interface.
For the forward modeling, Bouguer data extracted
from the XGM2019e_2159 dataset were employed. Thisdataset combines satellite gravity data from GOCO06
and terrestrial data, resulting in enhanced resolution ofshorter wavelengths associated with shallower depths.
By utilizing this powerful dataset, our aim was to gain
detailed vision about the subsurface structure andimprove our understanding of the geological characteristics specific to the studied area.

4 Results and discussion
4.1 Surface thermal anomalies map

The high-quality imagery (Figs. 4 and 5) presented
in this study was acquired during periods characterized by milder weather conditions, specifically toward
the end of winter and throughout the spring season.
The selection of these timeframes was motivated by
the reduced impact of cloud masking and limitations
related to satellite image dimensions. Figure 4 provides
an overview of the land surface temperature (LST)

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evolution observed in February, while Fig. 5 depicts the
LST patterns recorded in May. To explore the potential relationship between hot springs and temperature
anomalies detected in the LST data, a straightforward
overlay analysis was conducted using the geographicalcoordinates of various hot springs identified in (Zaher
et al. 2018) (Table 1). Notably, the primary objectiveof this investigation is to assess whether the observed
LST variations are influenced by factors such as Moho
depth and crustal thickness. Specifically, we aim todetermine whether regions characterized by a thinnercrust and a shallower Moho are more likely to experience higher surface temperatures due to the elevatedthermal impact exerted by the asthenosphere. Subsequent sections of this study will address this criticalconcern and acquire perspective on this relationship.
The findings of our study align with the conclusions of (Al-Aghbary et al. 2022; Zaher et al. 2018).
We confirmed that regions with a thinner crust anda shallower Moho tend to experience higher surface
temperatures due to the increased thermal impact of
the asthenosphere. Moreover, our research yielded
novel view of the relationship between land surface
temperature (LST) variations and Moho depth, as wellas crustal thickness. (Al-Aghbary et al. 2022) used arandom forest regression model to estimate geothermal heat flow across Africa, finding that the Gulf ofAden, the Red Sea and the East African Rift System are
the locations which regarded to have highest values of
the geothermal heat flow. These regions, characterized
by a thin crust and a shallow Moho, allow for the heatfrom the asthenosphere to reach the surface more easily. Similarly, (Zaher et al. 2018) conducted GIS-based
analysis to identify geothermal resources in Egypt,
identifying the significant prospective locations forgeothermal growth, which also exhibit a thin crust anda shallow Moho.

Fig. 4 LST map of Egypt using sentienel-3B, a LST (27/2/2022); b LST (6/2/2022), c LST (1/2/2022); d an overlay between a, b and c to strength
the thermal anomalies, red dots show the locations of hot springs (Table 1) by (Zaher et al. 2018)

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

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Fig. 5 LST map of Egypt using sentienel-3B, a LST (29/5/2022); b LST (25/5/2022), c LST (24/5/2022); d an overlay between a, b and c to strength
the thermal anomalies, red dots show the locations of hot springs (Table 1) by (Zaher et al. 2018)

4.2 Final derived Bouguer map

Various factors contribute to the positive or negative
effects on gravitational force, including the presence of
mountains, rifts, sedimentary basins and intrusive igneous bodies. In the case of Egypt, the gravity anomalies
are complex and result from a combination of these factors. Additionally, the most significant gravity anomalies in Egypt stem from the presence of mountains,rifts and sedimentary basins. The study area analyzed
Bouguer anomaly values, as depicted in Fig. 6, ranging
from − 100 to 300 mGal, reflecting density variations
and the geological structure of the underlying rocks. The
onshore region, encompassing the Nile River and southwest Egypt, exhibits negative values of Bouguer anomaly
because of existence of granitic crust, known for its lower
density compared to oceanic crust.
Conversely, the offshore areas including the Mediterranean coastline, the northern portion of the Nile Delta
and the Red Sea, display positive Bouguer anomaly values. This occurrence can be attributed to the existence ofmafic oceanic crust, which possesses higher density thancontinental crust. The variations observed in Bouguer
anomaly values offer helpful understanding about thegeological composition and tectonic history of the study
area.
In contrast, the study identifies significant high gravity
values within the continental crust of Egypt, predominantly concentrated in the Western Desert. These findings align with prior investigations conducted by (Sobhet al. 2019; Abdelwahed et al. 2013) on earth gravity. Nevertheless, this study introduces a novel perspective by
exploring the correlation between these reduced gravity
values and the depth of the Moho. The outcomes indicate
a reduction in crustal thickness, implying the proximity of the mantle to the Earth’s surface. This association
between decreased crustal thickness, areas with hot
springs and elevated surface thermal anomalies brings
new revelations derived from this research.

Fig. 6 Egypt’s sediments-free Bouguer map; resolution (0.1)

4.3 
Moho depth derived from the inversion model.

The inversion process utilized sediment-free Bouguer
gravity data and seismic constraints as input, leading to
the generation of 25 Moho models with minimal meansquare error. The most favorable outcome was attained
when adopting 35 km as a reference depth of Moho anda density difference of 500 kg/m3 (Fig. 7). The gravity
residuals exhibited a value of -0.10 mgal as a mean and avalue of 4.40 mgal as a standard deviation. The resulting
models portrayed varying Moho levels ranging from 20
to 40 km, as illustrated in Fig. 8. Additional information
can be found in the supplementary materials.
Figure 8 showcases the final depth model of Moho,
generated through the gravity data inversion, which
produced the least mean square error. This model holds

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

great significance in studying the Moho depth and theunderlying crustal structure, particularly in areas where
the Moho boundary approaches the surface. It offers
important details about the characteristics of the region’s
geological composition and aids in further exploration
and analysis.
The results of our study, as well as the studies by(Sobh et al. 2019; Abdelwahed et al. 2013), show thatthe depth of the Moho in the Eastern Desert estimatedto be approximately 20 km, 22 km and 21 km, respectively, while the Moho depth in the Western Desert isestimated to be around 30 km, 33 km and 32 km, respectively. Furthermore, our study, along with (Sobh et al.
2019; Abdelwahed et al. 2013), suggests a Moho depthof approximately 40 km, 43 km and 42 km, respectively,in the Nile Valley. These additional details further support the robust Moho depth model, which serves as animportant tool for studying Africa’s crustal structure,
identifying geothermal resource potential and enhancing
earthquake prediction capabilities.
The findings from the Moho inversion process alignwith previous studies by (Al-Aghbary et al. 2022; Zaher
et al. 2018), indicating that the typical African Moho
depth fluctuates between 20 and 40 km including certain regions, such as the Red Sea and the East African
Rift, exhibiting shallower Moho depths. The final Moho
depth model provides specific measurements across
Africa, revealing that the Eastern Desert has the shallowest Moho at approximately 20 km, followed by theWestern Desert at around 30 km, while the Nile Valley
exhibits the deepest Moho reaching about 40 km. Thesevariations in Moho depth distribution can be attributed
to the complex tectonic history of the continent, as theongoing building of the juvenile East African Rift System
contributes to its shallower Moho, and the tectonicallyFig. 7 The relation between the three gravity inversion parameters.
The reference Moho depth ( Zref ); 35 km, the density disparity (Δρ);
500 kg/m3, and the regularization parameter; (1e−10). The coloredcontours show the mean square error. The grey rectangular indicates
our optimum fitting model

Page 9 of 15

Fig. 8 The optimum Moho depth map; derived with 35 km depth
and 500 kg/m3 density disparity

stable Western Desert accounting for its relatively deeper
Moho. Beyond studying crustal structure, the final
Moho depth model serves as a beneficial resource for
identifying regions with thin crusts and close proximity
to the asthenosphere, holding potential as geothermal
resources. Moreover, the model aids in the understanding of Africa’s tectonic history and facilitates futureearthquake prediction.
4.4 Forward modeling

For verification of the reliability of our inversion model,
we utilized 2.5D forward gravity profiles employing
Bouguer data derived from the XGM2019e_2159 combined model. Forward gravity modeling, owing to thesignificant density contrast associated with the Mohoboundary, proved to be a suitable technique for determining the Moho depth. To ensure full coverage, we
selected five profiles (Fig. 9a) oriented in different directions: E-W for profiles P1 and P2, NW–SE for profiles P3
and P5 and NE-SW for profile P4. These profiles, by fitting the observed and calculated Bouguer gravity data,
provide significant geometric information about thebasement interface, Conrad boundary and Moho boundary while minimizing data error.
To strengthen the credibility of our findings, weconstructed forward cross sections using reference
data obtained from previous research. This approachenhances the evidential support for our results and reinforces the overall robustness of our investigation.
In our study, the visual representing of the 2D profiles
with density-based coloring illustrate the correlation
between observed and calculated gravity values (Fig. 10).
Furthermore, the figure includes an assessment of theerror percentage, enabling a comparison of the Moho

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

Page 10 of 15

Fig. 9 a Sediments-free Bouguer map extracted from XGM model; red lines present the positions of the density model profiles P1, P2, P3, P4
and P5, b the Moho depth map revealed from inversion; red lines present the positions of the Moho depth inversion model profiles I1, I2, I3, I4, I5
and I6depth between the inverse and forward models. This
comparative analysis validates the accuracy and reliabilityof the inversion methodology employed in our study.
The cross sections unveil an increase in crustal thickness from west to east, with the Moho depth progressively deepening from 30 km in the west to 35 km in theeast. Profiles P1 and P2 exhibit variations in sediment
thickness, while profiles P3, P4 and P5 provide discoveries on the structure beneath the Sinai Peninsula and the
diagonal structure of Egypt.
4.5 Model uncertainties

To assess the impact of inversion parameters, reference
depth (Zref ) and density contrast (Δρ) on the estimationof Moho depth, we conducted an analysis of uncertainties. For (Zref ), we employed a value of 35 km with a stepincrement of 2.5 km for the inversion range. Regarding
the density disparity (Δρ) at the reference depth 35 km,
we calculated values by subtracting 450 kg/m3 and adding 50 kg/m3.
The uncertainties and their corresponding results are
summarized in Table 3. Figures (9b and 10) present a
cross-comparison between the Moho depth obtained
from the inversion and the other which are derived fromforward density modeling. This comparison demonstrates the consistency between the forward model andthe inverted model.
Figure 11 illustrates the misfit in Moho estimation,comparing between the seismic-based and gravity-basedmodel. The frequency differences range from -5 to 5 km,
with 0.10 km mean and 4.4 km standard deviation.
4.6 
Moho depth correlated to hot springs.

Our objective was to investigate any possible links or
associations between the subsurface Moho boundary andthermal anomalies in the research area by considering
the data on Moho depth and the spatial distribution of
hot springs as depicted in Fig. 12. This approach involved
merging the Moho depth estimates obtained from theinversion process, based on the least mean square error,
with the distribution of hot springs (thermal anomalies).
The purpose was to establish a conclusive relationshipbetween these two factors.
By incorporating the information regarding the Moho
depth and the spatial distribution of hot springs, weaimed to uncover any potential connections or correlations between the subsurface Moho boundary and theoccurrence of thermal anomalies in the study area. This
integrated analysis can provide beneficial knowledge
about the geodynamic processes and geological characteristics that contribute to the formation of hot springs,
thereby contributing to a thorough understanding of theunderlying mechanisms at play.
Our finding coincided with the previous estimation of the Moho’s depth, such as (Youssof et al. 2013),(Baranov et al. 2023) and (Alemu 2024). The findings ofthis extensive investigation have substantial ramifications for the exploitation of geothermal resources and theBekhit et al. Progress in Earth and Planetary Science(2024) 11:39

Page 11 of 15

Fig. 10 2D cross sections of sediments-free Bouguer map extracted from XGM model and equivalent depth profiles extracted from the inversion
model, obtained in Fig. 9. The cross sections letters (from a to e) positions are shown in Fig. 9

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

development of electricity in Egypt. The center section of
the country, specifically between 25 and 30 N, contains
strategic spots for targeted geothermal exploration andresource assessment. These places have been determined
to have Moho depths ranging from 32 to 35 km.
On the other hand, the relatively shallow depths of the
Moho in these areas indicate the existence of higher thermal gradients and greater heat transfer from the Earth’s
interior. Consequently, this suggests a greater probabilityof discovering usable geothermal resources that might beutilized for generating power or providing direct heat. Thisapproved by (Zaher et al. 2018) when they related the hotsprings in the center of the western desert with the heatflow due to the thinning of the crustal thickness in thearea of hot springs. Additionally, this finding gives us a
more comprehension of the fundamental geological andtectonic elements that govern the presence of geothermal
resources, such as faulting process and sedimentary basins
distribution that have been explained in (Meshref 2017),(El-Dakak et al. 2021) and (Abdel-Fattah et al. 2019).
Table 3 Uncertainties calculation
Uncertainty

With

Mean (km)− 50 kg/m3 Δρ

(450–50) kg/m3 at 35 km− 0.280.75+ 50 kg/m Δρ

(450 + 50) kg/m3 at 35 km

0.22

0.60

− 2.5 km Zref

(35–2.5) at 550 kg/m3− 2.480.10+ 2.5 km Zref

(35 + 2.5) at 400 kg/m3

2.48

0.12

3

Standard
deviation

Page 12 of 15

Fig. 12 The Moho depth revealed from inversion, red dots show
the locations of hot springs (Table 1) by (Zaher et al. 2018)

5 Conclusion
The primary objective of this study was to identify potential geothermal locations in Egypt by analyzing thedepth of the Moho boundary. The Moho is the boundary
between the Earth’s crust and upper mantle. It is known
for having significant differences in physical attributes,
such as temperature gradients. In the initial phase ofthis study, we examined thermal anomalies on the land
surface alongside the distribution of hot springs. Subsequently, we performed a seismic constrained inversion ofsatellite gravity data using Bott’s method with Tikhonov

Fig. 11 a Differences in Moho depth between gravity derived and seismic derived, b the misfit histogram between gravity and seismic datawith -0.10 km mean and 4.40 km standard deviation

Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

regularization to generate a Moho depth map of Egypt
with minimum mean square error. To validate the results
obtained from gravity inversion, we conducted 2D gravity forward modeling on five profiles.
To assess the uncertainty of our conclusions, we compared the seismic and gravity-derived Moho models.
The mean difference is 0.10 km, indicating an excellent fit between the two. We also compared cross sections between the inversion and forward gravity-derived
Moho models, resulting in an exact match between both.
The Moho depth model indicates that the southern andwestern regions of Egypt exhibit greater depths, whereas
the eastern and northern areas, encompassing the Mediterranean, the Red Seas, display shallower depths. The
Egypt’s southern shows that the Moho depth varies
between 35 and 39 km, with a deepest point located at
39 km beneath the southwestern region. The Moho depthof Sinai Peninsula ranges from 35 km at the south to ashallower depth of 30 km at the north. The Moho depthof the Eastern Desert ranges from 31 to 35 km.
Our research shows that regions in the central part of
Egypt, between 25 and 30 degrees north, that have a significant quantity of thermal activity and hot springs, arecorrelated with Moho depths ranging from 32 to 35 km.
Regions in Egypt with shallower Moho depths are more
likely to have higher thermal potential because of thethinning of the Earth’s crust in those areas.. We suggest
exploiting these regions with Moho depths ranging from32 to 35 km to build geothermal power facilities. Exploiting Egypt’s geothermal energy potential in these key
areas might yield favorable environmental outcomes by
mitigating emissions that contribute to global warming
and societal benefits by delivering clean and dependable
electricity to local populations. In summary, the findingsof this study provide beneficial perspectives for future
geothermal exploration and development endeavors in
Egypt. Understanding geothermal resources’ distribution and properties can provide valuable information
for making policy decisions and developing investment
plans. This knowledge can help Egypt in its efforts to shift
toward a more sustainable and renewable energy generation. To further explore the areas indicated, we will nextcompute their geothermal heat flow, which represents
the transfer of heat from the interior of the earth to its
surface.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​024-​00639-6.
Supplementary Material 1.
Supplementary Material 2.

Page 13 of 15

Acknowledgements
The authors thank the National Research Institute of Astronomy and Geophysics (NRIAG) for the constraining geophysical data. Also, we thank the Egyptian
Knowledge Bank (EKB) and Science and Technology Development Fund(STDF) for funding this paper’s publication. And we appreciate the usefulfeedback from the editor and anonymous reviewers, which assisted in the
improvement of this paper.
Author contributions
All authors participated in the interpretation of results, offered essential feedback and endorsed the final manuscript. Ahmed Mohamed Bekhit specificallyconducted the analysis and inversion of the gravity data, while Mohamed
Sobh assisted with the modeling of the gravity data. Mohamed Abdel Zaher
aided in the analysis of satellite data and the interpretation of thermal anomalies. Tharwat Abdel Fattah and Ahmed I. Diab contributed to the conception
and design of the study, as well as overseeing the entire project. They also
assisted in drafting and revising the manuscript.
Funding
Open access funding provided by The Science, Technology & Innovation
Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank
(EKB). The paper publication will be funded by the Egyptian Knowledge Bank
(EKB) and Science and Technology Development Fund (STDF) based on the
publishing agreement between Egypt and Springer-Nature.
Availability of data and materials
The land surface temperature data of Sentinel-3 satellite downloaded from(https://​www.​onda-​dias.​eu), the topography data revealed from the ETOPO1
model, the gravity data extracted from GOCO06 and XGM2019 models all supported by ICGEM international Centre global models at GFZ Potsdam (http://​icgem.​gfz-​potsd​am.​de/​home), the seismic and hot-springs data obtainedby the National Research Institute of Astronomy and Geophysics (NRIAG). All
inversion models and the optimum final Moho depth model are available on
the link (https://​zenodo.​org/​recor​ds/​10719​349).

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors imply that there is no conflict of interest with any authors or others. Also, this manuscript has not been published and is not under consideration for publication elsewhere. The authors have no relevant financial or
non-financial interests to disclose.
Received: 27 February 2024 Accepted: 6 June 2024

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Bekhit et al. Progress in Earth and Planetary Science(2024) 11:39

Ahmed Mohamed Bekhit is a master student at Alexandria University, specified in geodynamics and lithospheric modeling.
Mohamed Sobh is the postdoctoral researcher at Leibniz Institute for Applied Geophysics, specified in global geophysics andgeodynamics.
Mohamed Abdel Zaher is a professor of applied geophysics andspecified in geothermal prospection at National Research Institute of
Astronomy and Geophysics (NRIAG).
Tharwat Abdel Fattah
Alexandria University.

is a professor of applied geophysics at

Ahmed I. Diab is an associate professor at Alexandria University
and interested in satellite gravity.

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