3.0.1 Spatial modelling of Geothermal conditioning factors (GCFs) Result
The GCFs are critical for prospective geothermal zonation in the research region. A fishnet is generated spatially on all geothermal potentiality conditioning factor thematic maps to extract data at consistent locations correctly. The fishnet is created using the data management tool in the ArcGIS program. The fishnet ensures that the points in the research region are uniformly distributed and that interpolation is rapid. Figure 4d depicts the fishnet template map of the research region, which was employed for statistical analysis in the study area. Two thousand five hundred (2500) score characteristics were developed throughout the research region to construct a grid for the easy and uniform data collection on each GCFs thematic map.
3.0.1.1 Surface Albedo (SA)
According to Shannon et al. (2010), surface albedo is an essential parameter in geothermal prospecting. During geothermal prospecting, regions containing materials with high energy absorption ability emit more significant solar radiation than bright surfaces, resulting in high terrestrial emittance readings that might not include a geothermal heat flux componentAs a result, SA values were evaluated in this work as the output of Eq. (2) on processed Landsat 8 OLI/TIRS images in the ENVI 5.3 software with computed SA values ranging from 0.35 to -0.00577 (Table 1, column 5). Table 2 shows the five classed groupings of SA zones in the research region. The calculated areal extents of the classes are 3568.96, 4804.15, 4599.55, 4280.29, and 3552.13 km2, respectively, corresponding to Very high, High, Moderate, Low, and Very low SA zones. Figure 5a and Table 2 reveal that the area has a high to moderate SA value predominance.
3.0.1.2 Normalized Difference Vegetation Index (NDVI)
NDVI is a critical measure for calculating surface radiant temperature, according to Lo et al. (1997). It is the ratio of the reflectivity differences between the near-infrared band (4) and the red band (3). The NDVI values were calculated in ENVI.5.3 using Eq. (1) based on the bands evaluated findings from the processed Landsat 8 images. The research area's calculated NDVI values vary from − 0.09 to 0.57 (Table 1, column 4). The area depicted on the NDVI map (Fig. 5b) was created using the GIS tool to solve Table 1 Column 4. According to the map, the area is primarily linked with a moderate to high NDVI value (0.04). According to NASA (2009), Qin et al. (2011), and Mia et al. (2012), this area has a high rate of heat loss due to a high rate of transpiration and latent heat transfer, resulting in a lower surface temperature within the area. Based on a qualitative comparison with existent warm springs in the research region, zones with moderate to high NDVI values were deemed likely geothermal anomalies.
3.0.1.3 Slope (S)
From the processed SRTM-DEM image, the slope degree is determined. The slope values ranging between 0–85 degree is as presented in Table 1. By re-analyzing column 10 of Table 1 result in a GIS environment, the area slope map (Fig. 5c) was produced. According to Table 2, the area is classified into five groups. High slope values characterize the central part of the study area. These zones were predicted to have high geothermal potential. This is in line with Zhang et al. (2012) contribution, which found that a high slope location might considerably accelerate the formation of linear features such as faults, fractures, and cracks. As a result, the high values zone is closely related to fluid channels that can help circulate hydrothermal fluids. Low slope values indicate low geothermal potential zones in the northeastern and southeastern parts of the research region.
3.0.1.4 Lineament density (Ld)
Lineament is defined as observable geomorphic linear features typifying weakness. Lineaments are probably the result of tectonic activities on geological structures resulting in fractures or lithologic contacts (Chowdhury et al., 2009). Lineaments were identified by visual interpretation of SRTM-DEM processed images. Figure 5d shows the spatial variation of lineament density within the study area ranging from 0 to 0.00279 (Table 1, column 12). Lineament density has a direct relationship with the geothermal potentiality of an area since the geothermal system is controlled by faults that act as conduits for the convection movement of hydrothermal fluid within the subsurface. According to Fig. 5d, the area extent of 5162.47, 3646.35, 2288.06, and 1066.30km2are for the classes, namely: the very low, low, moderate, and high, respectively, were inferred from Fig. 5d and were classified into four classes using the natural break approach (Jenk, 1967) namely; very low, low, moderate and high with area extent of 5162.47, 3646.35, 2288.06 and 1066.30km2 respectively. Lineament density has a direct relationship with the geothermal potentiality of an area since the geothermal system is controlled by faults that act as conduits for the convection movement of hydrothermal fluid within the subsurface. According to Saepuloh et al. (2018), highly fractured zones are potential zones favorable for the convectional movement of geothermal fluid during geothermal prospecting.
3.0.1.5 Land Surface Temperature (LST)
The main contributors to surface temperature are solar radiation, which causes uniform warming of the surface, and heat within the earth, which causes local temperature increases. Agreeing with Zhang et al. (2012), LST directly correlates with local geothermal potential. That is, the higher the temperature, the more likely there is geothermal energy in the area. The computed LST values range from 23.430C to 470C (Table 1, column 11). Figure 5e was classified into five classes using the Natural Breaks Approach (Jenks, 1967), namely: very low (23.43–26.3), low (26.31–27.68), moderate (27.69–29.35), high (29.36–31.47), and very high (31.48–47.0), with area extents of 3372 km2, 47001 km2, 2347 km2, 1265 km2, and 713 km2. According to Fig. 4.6, the central and northern parts of the map have moderate to high elevated LST values. On the other hand, the southeastern and western parts of the study area are dominated by low LST values. Zones with high LST values have a high potential for geothermal resource exploration. This is in line with the submissions of Zhang et al. (2012), who established that places of high temperature are associated with high geothermal potential and thus submit that LST is directly proportional to the geothermal potentiality of an area.
3.0.1.6 Curie depth point
Assessment of regional CDP variability provides valuable information about regional heat fluxes, deep temperature distributions, and geothermal potential energy potential within the region (Tselentis, 1991). Table 1, column 9 presents the CDP computed values using Eq. 4. The CPD values ranging from 10.32 to 19.58 were processed in the ArcGIS environment produced in Fig. 5f. Using the Natural Breaks Approach (Jenks, 1967), Fig. 5(f) was divided into five categories: very high (15.49–19.58), high (14.03–15.48), moderate (13.24–14.02), low (12.29–13.23), and very low (10.32–12.28). The area coverages of each class are 416.21 km2, 5206.41 km2, 3995.31 km2, 2368.41 km2, and 260.48 km2, respectively. The eastern and western regions of the map have moderate CPD values, while the eastern part of the research area has pockets of low CDP values. High CPD values occur in the northwestern and central regions of the study area. The southern region of the CDP map is characterized by low to very low CPD values. The low to very low CPD was predicted to have high geothermal potential. This agrees with Aydin and Okusum (2010) submission, who reported that zones characterized by shallow Curie point depth are associated with high geothermal potential.
3.0.1.7 Heat flow (HF)
The HF was computed employing Eq. (8). Heat flow values estimated vary from 153.3654 mW/m2 to 231.54 mW/m2 (Table 1, column 8). The classes of Fig. 5g regionalized is as detailed in Table 2. According to Abraham et al. (2014); Mono et al. (2018), this factor often provided a good insight into an area's temperature flow rate. According to the heat flow map (Fig. 5f), the moderate to high heat flow regions are characterized majorly by the southern and north-central parts of the area, while some pockets of high heat flow values occur in the northeastern part of the study area. According to Abraham et al. (2014), Khojamli et al. (2015), and Mono et al. (2018), moderate to high heat flow regions are possible geothermal potential zones. In line with the study of Abraham et al. (2014), the high heat flow values within the subsurface could be attributed to the presence of radioactive elements such as thorium and potassium or as a result of a continuous flow of molten magma arising from tectonics activities.
3.0.1.8 Geothermal gradient (GG)
The GG values were computed via Eq. (9). The computed GG values range from 61.34°C/km-92.62°C/km (Table 1, column 7). Geothermal gradient is the variation in temperature with depth, increasing with depth within the earth's crust. Figure 5(h) shows the geothermal gradient map of the study area. The spatial distribution of the geothermal gradient (Table 1) was classified into five classes (Table 2). The moderate to high geothermal gradient zones are possible geothermal potential zones within the study area (Abraham et al., 2014). Moderate to high GG values cover the southern, northcentral, northeastern, and southeastern regions. The northwestern and central regions in the area are characterized by very low to low GG. values. According to Mono et al. (2018), high geothermal gradient places indicate high geothermal potential. As a result, the geothermal gradient is proportional to the geothermal potential.
3.0.1.9 Land use land cover (LULC)
The LULC map (Fig. 5i) displays the land use in the study region. This factor often gives a good insight into the functional usage and physical coverage of the earth's surface (Homer 2011, Chen 2018). LULC has a direct relationship with geothermal potentiality mapping, as it accounts for the thermal noise emanating from urban heating, the high transpiration rate, the larger latent heat transfer, and the high photosynthesis rate (Mia et al., 2012; Qin et al., 2011; NASA, 2009). Figure 5 depicts five major land use land cover classes, namely, urban area, water body, vegetation, bare soil, and rocks/outcrops, with respective area extents of 1017.11 km2, 17.45 km2, 2437.22 km2, 3527.36 km2, and 5392.56 km2. The research region is dominated by rocks and outcrops that potentially contain hydrothermal fields, as shown in Fig. 5(i). The total accuracy of the land use and land cover map was assessed to be 73%, with a Kappa coefficient of 64%.