GIS has an extremely diverse range of applications in fields of study related to spatial information. In particular, in the context of this study, GIS-based resource evaluation is a significant and generally economical tool for distinguishing areas of interest for geothermal investigation. GIS allows different layers of spatial data to be examined and provides tools for investigating and displaying the interrelationships between TL. Recently, GIS-based multi-criteria decision analysis (MCDA) techniques have been widely utilized to produce geothermal suitability maps (e.g., Tüfekci et al., 2010, Noorollahi et al., 2007, Trumpy et al., 2015, Abdel Zaher et al., 2018, Abuzied et al., 2020). MCDA methods involve weighted and fuzzy overlay techniques. Fuzzy logic overlay analyses follow the same general steps as weighted overlay analysis, but with additional emphasis on certain steps. For example, in the fuzzy overlay method, the input is transformed (i.e., scaled) and the resulting values define the possibility of membership in multiple sets, whereas, the weighted overlay method uses a relative preference scale and weights each input accordingly (Esri, 2020). The weighted overlay technique avoids the risk of disregarding potential sites because of sharp limits between spatial classes; furthermore, it gives clearer outcomes and provides reliable assessments from unsatisfactory to reasonable sites (Aliki and Hatzichristos, 2019). The weighted overlay model allows various issues to be considered that are affected by multiple factors with different weights applied to each of them. The outcomes are expected to give a more in-depth assessment and provide more detailed information, as the weighting method ranks areas based on their appropriateness.
In this study, a GIS-based MCDA technique and a weighted overlay method are used to integrate various datasets, including geological, geophysical, and surface manifestations of geothermal activity. The data was processed digitally to constitute five criteria layers: distance to fault lines, geologic units, distance to surface manifestations (i.e., hot springs, volcanoes, and geysers), distance to seismic epicenters, and the value of heat flow, as derived from aeromagnetic data. An analytic hierarchy process (AHP) method was applied to weight each criterion. This is followed by combining each standardized, weighted layer, using the GIS to gain an overall geothermal potential map. The results were verified using comparison with the locations of known geothermal wells. Figure 2 shows a workﬂow chart of the input data and the process of defining the geothermal potentiality map of the African continent.
Standardization of criteria is the first step in preparing the raster layers. In this approach, the user-selected layers are assigned a common numeric range by classifying their attributes into discrete classes. In this paper, criteria layers were attributed to different classes on a scale from 1 to 9, based on the decision maker’s experience-based suitability evaluation. The lower values represent less favorable areas, while higher values represent areas of greater geothermal potential.
Criteria Weight Assignment
An important step includes the assignment of criteria weighting coefficients. The analytic hierarchy process (AHP) is the most frequently used method for weighting coefficient calculation (Bana e Costa, 2016; Meng et al., 2021; Saaty, 1977). Saaty’s (1977) method was applied to compare the evaluation index and to study its importance (Table 1). The established model helps to estimate the weighting for each index. A matrix is constructed according to Saaty (1994) which helps to determine the importance of the layer’s elements using the following equation:
where xij, (i, j = 1, 2, 3,…, n) is the ratio of the importance of criteria i to that of criteria j.
The geometric mean method (Perzina and Ramíka, 2014) was used to calculate the weights of each individual criterion based on the values in the pairwise comparison matrix. The consistency index (CI) of the evaluation matrix is defined as follows:
where n is the order of the pairwise comparison matrix and λmax is the highest eigenvalue of the matrix. Then the consistency ratio (CR) is calculated via the following equation:
where RI is represented by average CI values gathered from a randomly generated pairwise comparison matrix. The use of CR allows one to evaluate inconsistency regarding the judgments within the comparison matrix. The suggested value of the CR should be no higher than 0.1 (Saaty, 1988); if the CR value is less than 0.1, this indicates that the judgments were consistent.
The MCDA GIS method model is developed with on overlapping multiple criteria layers after multiplying the standardized layers by their corresponding weights. The adding of the processed grid layers leads to a composite value that represents the geothermal potential map for the entire study area.
Analyses of thematic layers (TL) of evidence
Geological and Structural TL
The first TL of evidence is obtained from the lithological and structural map of Africa, which provides evidence of the genesis of geothermal phenomena. The distribution of the Phanerozoic and Precambrian rocks was digitized from the geological map of Africa (Figure 3) at a scale of 1:10 million (Thiéblemont et al., 2016). Young volcanoes or volcanic rocks of Pleistocene–Recent age may act as a heat source, while the presence of faults and fractures creates fluid pathways (Poux and Suemnicht, 2012). Pleistocene-age volcanic rocks were also integrated into the geological and structural TL (Persits et al., 1997), as they have a strong correlation with the geothermal manifestations of northern Algeria and eastern Africa.
Regionally continuous basement exposures exist in the eastern part of the African continent along the East African Rift System (EARS) and the western coast of the Red Sea, the eastern coast of the Atlantic Ocean, in the western craton of the African continent, and East Madagascar. The crystalline basement rocks of Africa contain three significant associations: (1) the granite–gneiss–greenstone and other high-grade metamorphic assemblages which make up the Archaean cratons; (2) strongly deformed mobile belts (mostly of Proterozoic age); and (3) anorogenic intrusions and extrusive products, which include rift-related Phanerozoic magmatic and volcanic rocks (Key, 1992). South Africa also has an abundance of granites and gneisses of different ages and chemical compositions, a large portion of which show anomalous radioactivity and above-average heat flow. Despite the influence of basement rock lithology, the presence of intensive zones of rock fracturing and faulting is the main control on the activity of geothermal fluids. These faults are mostly associated with recent tectonic events in the African Plate, in particular, Cenozoic faulting along tensional fractures of the EARS.
Faults and fractures are important in controlling geothermal potential since hydrothermal fluids migrate most effectively through faults (Hanano, 2000). Faults on the geological map of Africa (Thiéblemont et al., 2016; Meghraoui et al., 2016) were collected and digitized (Figure 3). Much of the geothermal activity in Africa is concentrated in the east of the continent around the EARS, where continental rifting is associated with geothermal systems with magmatic and volcanic heat sources. The EARS is one of the most major active extensional tectonic regimes on Earth; the area is characterized by prominent geothermal potential spatially associated with the Quaternary volcanoes and geysers located along the rift axis.
Volcanic and Geothermal Surface Manifestations TL
All available surface data related to geothermal activity were collected to characterize the geothermal systems. These data comprise all geothermal manifestations (i.e., volcanoes, hot springs, and geysers) (Figure 1). The hot spring locations in Africa were gathered from diverse sources (Ait Ouali et al., 2019; Waring, 1983; Saibi et al., 2006) and were merged in the GIS model. These surface manifestations were used as the fourth TL of evidence in the GIS model. Volcanic elements such as craters, calderas, and active or young volcanoes are also direct indicators for the existence of an underground heat source. The south and north of Africa have not been considered until now as feasible areas for geothermal energy production due to their tectonic stability (Dhansay et al., 2014; Enerdata, 2013); however, some countries in the north and south of Africa (e.g., Egypt, north Algeria, and South Africa) have surface thermal manifestations, such as hot springs of various temperatures.
The United Nations Environment Program and the Infrastructure Consortium for Africa estimate a geothermal potential capacity of more than 20 GW of geothermal energy across Eastern Africa (Teklemariam, 2018), which has encouraged countries, including Comoros, Eritrea, Djibouti, Rwanda, Uganda, and Tanzania, to conduct preliminary investigations for geothermal resources. Ethiopia has a future plan to reach 1 GW production from geothermal energy by 2021. Additionally, Uganda, Burundi, and Zambia are working to establish new small-scale geothermal power plants (Hafner et al., 2018). Table 2 summarizes the geothermal activities, temperature gradients, and heat flows of different African countries, compiled from the previous literature.
Seismic Activity TL
The seismic database of Africa was obtained from numerous sources, including the United States Geological Survey (USGS) (https://earthquake.usgs.gov/earthquakes), the International Seismological Center (ISC) (http://www.isc.ac.uk) and the Egyptian Seismological Center (ENSN, 2014) (Figure 4). “Seismic events” or epicenters refer to the existence of active faults which, as noted above, play an important role in geothermal systems by providing the permeability pathways required to bring waters heated at depth to close to the surface.
The distance to seismic activity (epicenters) was utilized as the fifth TL of evidence to map the geothermal potential of Africa. The African plate experiences frequent seismic events, the most recent of which are principally situated along rift zones, active volcanic fields, thrust and fold mountain belts, and offshore transform faults. Some regions in Africa are relatively aseismic, whereas, others have experienced disastrous seismic events. The most conspicuous tectonically active structures are located in the east of Africa, where the intracontinental EARS is located (Kebede & Kulhanek 1991; Ring, 2014). This rift system is the source of most earthquakes in the African continent and extends from the Afar Triangle through Ethiopia to Mozambique; these areas have been volcanically and magmatically active since rifting began in the Miocene (Wafula, 2011). In contrast, the western and central regions of Africa show generally subdued seismicity.
Heat Flow TL Calculated from Magnetic Data
The aeromagnetic map of Africa was used to calculate the regional Curie Point Depth (CPD) and geothermal structure in the form of the heat flow map of Africa using the spectral centroid technique. Aeromagnetic information was acquired from the various datasets that were combined under the “African Magnetic Mapping Project” (AMMP) (Getech, 1992; Green et al., 1992). The CPD, which is the depth at which ferromagnetic properties of minerals change to paramagnetic due to rising temperature (>500 °C), was estimated using the centroid technique which involves spectral analysis of the aeromagnetic data (using a window size of 1000 km2). The centroid technique depends on the appraisal of separated magnetic anomalies and the statistics of magnetic groups (Spector and Grant, 1970; Bhattacharyya and Leu, 1977). The depth to the top of the magnetic source (Zt) is computed from the slope of the longest wavelength portion of the spectrum, and the depth to the centroid (Zo) is calculated from the spectrum divided by the wavenumber “|k|”. The base of the magnetic source (Zb), assumed to be the CPD, can then be obtained from the equation Zb = 2Zo-Zt (Okubo et al., 1985).
The temperature gradient was calculated using the basal depth Zb and the Curie point temperature of 580 °C: dT/dz = 580 °C / Zb (Maden, 2010). The heat flow map of Africa (Figure 5) was then computed using the formula: q =λ (dT/dZ) = λ (580 ºC/Zb); where λ is the coefficient of thermal conductivity (assumed to be 2.0 W/moC, which is representative of Precambrian lower crust (Seipold, 1992)). The calculated heat flow values from borehole bottom hole temperatures for 23 African countries were compared with the values derived from CPDs in order to confirm the results (Figure 5). This comparison showed an overall coefficient of correlation of 0.7. The highest heat flows are found over the eastern part of the African continent and relate to the Afar Triangle and EARS, which comprise highly volcanic areas. High heat flows continue northwards along the Red Sea rift, but also southwards to the Kalahari craton. The northwestern margin of Africa bordering the Atlas Mountains is also a broad area of high heat flow. Individual intracontinental hotspots correspond to plume activity in the Tibesti (Chad) and Darfur (Sudan) regions.