Reprocessing of regional-scale airborne electromagnetic data is used to build a 3D geological model of the Nasia sub-basin, northern Ghana. The resulting 3D geological model consistently integrates all the prior pieces of information brought by electromagnetic data, lithologic logs, ground-based geophysical surveys, and geological knowledge of the terrain. The geo-modeling process is aimed at defining the lithostratigraphy of the area, chiefly to improve the stratigraphic definition of the area, and for hydrogeological purposes. The airborne electromagnetic measurements, consisting of GEOTEM B-field data, were originally collected for mineral exploration purposes. Thus, those B-field data had to be (re)processed and properly inverted as the original survey and data handling were designed for the detection of potential mineral targets and not for detailed geological mapping. These new geophysical inversion results, compared with the original conductivity–depth images, provided a significantly different picture of the subsurface. The new geophysical model led to new interpretations of the geological settings and to the construction of a comprehensive 3D geo-model of the basin. In this respect, the evidence of a hitherto unexposed system of paleovalleys could be inferred from the airborne data. The stratigraphic position of these paleovalleys suggests a distinctly different glaciation history from the known Marinoan events, commonly associated with the Kodjari formation of the Voltaian sedimentary basin. Indeed, the presence of the paleovalleys within the Panabako may be correlated with mountain glaciation within the Sturtian age, though no unequivocal glaciogenic strata have yet been identified. Pre-Marinoan glaciation is recorded in rocks of the Wassangara group of the Taoudéni Basin. The combination of the Marinoan and, possibly, Sturtian glaciation episodes, both of the Cryogenian period, can be an indication of a Neoproterozoic Snowball Earth. Hence, the occurrence of those geological features not only has important socioeconomic consequences – as the paleovalleys can act as reservoirs for groundwater – but also from a scientific point of view, they could be extremely relevant as their presence would require a revision of the present stratigraphy of the area.
The present research demonstrates the effectiveness of the reprocessing and proper inversion of existing airborne electromagnetic (AEM) data – more specifically, GEOTEM B-field measurements – for the data-driven inference of subsurface geology. More specifically, the AEM results are employed to develop a 3D geological model for subsequent hydrogeological conceptualization and scenario simulations of groundwater recharge and abstraction (under different environmental and anthropic stresses) in the partially metamorphosed sedimentary Nasia Basin (a sub-catchment within the White Volta Basin in northern Ghana). In fact, the overall objective of the research is to develop a decision-support tool for understanding groundwater occurrence to facilitate the efficient development and optimization of the water resources in the basin within the framework of the GhanAqua project.
The use of groundwater resources for crop irrigation offers an opportunity for a buffer against the unremitting impacts of climate change in northern Ghana, where peasant farming is the mainstay of livelihood. The development of groundwater resources to support irrigation endeavors is particularly important because of erratic rainfall patterns during the rainy season and high temperatures and evapotranspiration rates in the dry season, which render surface water resources unsustainable for irrigation water (Eguavoen, 2008). Erratic rainfall patterns in the region in recent times have affected crop production and the sustainable livelihoods of communities. Hence, improved access to groundwater resources for year-round irrigation would boost agricultural development and offer increased employment possibilities in the area. However, over the years, access to sustainable groundwater resources has been hampered by the lack of sufficient knowledge of the local and structural geological setting. Such knowledge is crucial to the understanding of the hydrogeology and groundwater storage conditions and would be crucial for sustainable resource development.
Generally, the difficulty in defining and effectively characterizing subsurface geological conditions in an area such as the Voltaian sedimentary basin hinges on the unavailability of reliable data (e.g., lithological logs of deep boreholes) and the limitations inherent in conventional ground-based geophysical techniques (e.g., poor spatial coverage and insufficient density). So, a multi-scale holistic approach, integrating the airborne geophysical insights with all the available lithological borehole logs and ground-based geological investigations, is shown to be essential for the development of an effective and coherent geological model to eventually be used for hydrogeological assessments.
Three-dimensional (3D) geological modeling based on specifically collected AEM data for hydrogeological applications is, in general, not new (Jørgensen et al., 2013, 2015; Høyer et al., 2015; Oldenborger et al., 2014), but as far as we are concerned, it has never been done before using B-field measurements. Additionally, geological modeling for hydrogeological application is novel in the West African subregion, even though the region has a rich database of preexisting AEM data from former mineral exploration surveys. Hence, the application of the presented workflow for the inversion of AEM data can potentially be extended to many areas in this part of the African continent and, in general, everywhere preexisting AEM data are available. This can help avoid the costs connected with the airborne data collection, which is often considered affordable for mineral exploration but prohibitive for groundwater mapping.
Besides the (re)use of the abovementioned geophysical datasets and in an attempt to address the main socioeconomic issues connected with an effective hydrogeological characterization of the Nasia Basin, the present research brings some contributions to the geological and stratigraphic knowledge of the Volta Basin. The lithostratigraphy of the sedimentary infill of the Volta Basin is still disputed (Blay, 1983; Affaton, 1990; Carney et al., 2008, 2010). However, there is an overall consensus on a subdivision into three groups (Affaton, 1990; Affaton et al., 1980, 1991; Bertrand-Sarfati et al., 1991): Bombouaka, Oti (or Pendjari), and Obosum. In this research, we provide possible insights on the delineation of the interfaces between the formations characterizing the Nasia portion of the Volta Basin, i.e., Bombouaka and Oti.
The approximately 5300 km
The area is characterized by relatively low relief in the south and a few areas of high elevation associated with the Gambaga escarpment to the north. The basin drains a left bank tributary of the White Volta, the Nasia River (Fig. 1a), and is underlain by sedimentary rocks of the Bombouaka and Oti–Pendjari groups of the Neoproterozoic Voltaian supergroup; it is comprised predominantly of variations of sandstones, siltstones, and mudstones (Carney et al., 2010). Detailed descriptions of the geologic units can be found in Carney et al. (2010) and also in Jordan et al. (2009).
A 3D geo-model of an area is a synthesis of all relevant geologic information available; during the construction process, it is essential to integrate and merge multiple data sources and scales in order to appropriately represent the different aspects of complex geologic systems (e.g., Dzikunoo et al., 2018; Rapiti et al., 2018; Jørgensen et al., 2017, 2015; Vignoli et al., 2017, 2012; Høyer et al., 2017). In this regard, the diverse kinds of data used in this study consist of (i) AEM data (namely, GEOTEM B-field), (ii) borehole lithological and geophysical logs, and (iii) preexisting outcrop analyses and geological information.
An important underlying consideration for the construction of a lithostratigraphic model is the definition of a conceptual model initially developed from the prior knowledge of the terrain (Fig. 1b). Interpretations from geophysical signatures are then tied into the conceptual model, followed by the development of a model framework with interpretation points and subsequently populated with voxels, each characterized by homogeneous attributes. Clearly, any piece of information brought in this specific case from the geophysics can and must be used, via a confirmation–rejection process, to refine the initial geological hypotheses (Tarantola, 2006).
A fixed-wing Casa 212 aircraft, equipped with a 20-channel GEOTEM multi-coil
system, was used to acquire the time-domain electromagnetic data (Fugro
Airborne Surveys, 2009a, b) with a line spacing of 20 km (flown at
042–132
Within the present study, the original B-field data have been reprocessed and inverted; this is to ensure the preservation of all the corrections applied to the raw data by Fugro and, contextually, to have the opportunity to consistently compare the new outcomes with the conductivity–depth images (CDIs) provided by the survey company as final deliverables (Fugro Airborne Surveys, 2009a, b). This comparison was necessary in order to estimate what could be gained by going through a complete reprocessing and inversion in terms of the reliability and accuracy of the subsequent (hydro)geological model(s). Since the data acquisition was originally focused on mineral targeting, the specifications of the survey and the choice of CDIs as deliverables were intended to optimize the detection, even at depth, of large conductivity contrast targets (typical for mineral exploration) with a potentially high lateral resolution. Conversely, for geological mapping purposes, the capability to retrieve even low-contrast conductivity features via proper inversion strategies and, at the same time, to reproduce the spatial coherence of the geological features is crucial. Therefore, it was important to double-check the effectiveness of the new inversion approaches and of the dedicated preliminary data conditioning.
In addition to the advantages discussed in Smith and Annan (1998), the
choice of B-field has some further benefits in terms of the signal-to-noise ratio
as the B-field is associated with data integration over time that can act as
some sort of stacking in time. Clearly, the data stacking can (and should) also be
performed in the other “direction”, that is, spatially along the
line of flight. In the workflow implemented in this research, a moving
window with a width variable depending on the considered time gate has been
used in a fashion similar to the one detailed, for example, in Auken et al. (2009) and Vignoli et al. (2015a) (but here the stacking window width is
frequency-dependent). This strategy allows for the use of (i) a narrower time
window at the early gates and (ii) a wider window at late gates where the
signal has, in any case, a larger spatial footprint. By doing so, we can
obtain the maximum spatial resolution at the near surface (where the signal
is stronger) and, contextually, improve as much as possible the
signal-to-noise ratio at depth (where the physics of the
method naturally average the information). In particular, the size
of the window utilized for the
With respect to the inversion – as the receiver of the GEOTEM system is
located in a towed bird – the altitude, pitch, and roll of the device were part
of the inversion parameters, and they were reconstructed by using the
The workflow describing the iterative interaction between geologists and geophysicists leading to the development of the 3D geological model consistently integrating all the diverse pieces of information available (geophysical data, prior geological knowledge, wells, etc.).
For the large majority of the Nasia sub-catchment, a smooth regularization has been used with extremely loose (compared with those generally used for the standard dB–dt data inversion; e.g., Viezzoli et al., 2010, 2013) lateral and vertical constraints. The result is a quasi-3D resistivity volume generated from B-field GEOTEM data that is significantly different (in terms of possible geological interpretation) from the original CDIs (Fig. 4).
Raw data collected from various sources can be interpreted in terms of spatial variations (providing information about the geometries) and/or in terms of the absolute value of the attribute retrieved from the data (characterizing not only the geometry of the features, but also their nature).
The spatial information from the geophysics was used to create a 3D geometry model. Geometric modeling involves two steps. The first concerns the development of a suitable geometric representation of the fundamental geological “framework”; the second relates to the discretization of this framework to provide control for the analytical computations within the numerical models used in the predictive modeling (Turner, 2006).
In the present research, the first stage in the geometric modeling involved the interpolation of inverted 1D AEM data (Fig. 5) into a 3D grid with an assigned search radius of 20 km and a cell size of 2 km for the regional data as well as a search radius of 500 m and a cell size of 100 m for the dense area. The assigned search radius should not be less than the spacing between flight lines to obtain a continuous electrical resistivity distribution (Pryet et al., 2011), but, at the same time, it needs to be small enough to prevent the smearing of possibly useful information. The second, more laborious, step involved constructing the surfaces that define the overall units. Here, both the AEM and borehole data were correlated with a particular stratigraphic unit, and the boundaries for that unit were drawn. This is necessary, and very tricky, since the electrical resistivity as it is inferred from the AEM cannot be unambiguously made to correspond to a specific lithology and/or stratigraphic unit. For this task, knowledge of the geophysical response behavior and the experience of geologists in outlining which signatures belong to which stratigraphic unit are clearly equally crucial. For instance, low resistivity signatures within the Bombouaka may belong to the Poubogou formation, whereas anomalies with similar low resistivity ranges within the Oti may belong to the Bimbila formation. Also for this reason, the tight interaction between geologists and geophysicists (through several iterations) has been found crucial for effective geo-modeling – for example, in interpreting geological features wherein the geophysical reliability is reduced as we get closer to the depth of investigation (DOI – the shaded portion at the bottom of Fig. 4b).
A 3D view of the B-field SCI results along the
Thus, the geo-modeling can be considered a way to compile, in a consistent manner, geological knowledge about the area, information from the dense geophysics (which acts as a “smart” interpolator between the available boreholes), and the other available data. In this respect, it is worthwhile to note that only boreholes that had a distance smaller than 1–3 km from the regional flight lines were considered sufficiently representative for the geophysical interpretation.
The outlined boundaries were then used in the next stage for populating the model grid (Ross et al., 2005; Sapia et al., 2015; Jørgensen et al., 2013). Populating the model grid is done by adding and editing voxel groups based on a cognitive approach (Fig. 6; Høyer, et al., 2017, 2015; Jørgensen et al., 2013).
3D geological model of the Nasia sub-basin resulting from the combined interpretation of the B-field airborne data, the prior geological knowledge of the area, and the available wells.
Figure 4 shows an example of the comparisons between the original CDIs and
the new inversion obtained with the discussed smooth SCI approach applied on
the B-field GEOTEM data. The differences are evident. Not surprisingly, the
CDI result is characterized by higher lateral variability, as each sounding
is converted into a resistivity profile independently, while the SCI, by
definition, enforces some degree of spatial coherence. The more prominent
CDI's lateral heterogeneity is clear not only on the NE side of Fig. 4a
in the shallow portion of the section, where distinct resistive inclusions
are detected, but also at depth along all the flight lines where there are
spurious lateral oscillations of the electrical properties. The SCI result
is laterally more consistent; however, this does not prevent the
reconstruction of a resistive body (associated with the hotter colors), at
a distance of approximately 10 km (circled in red – Fig. 4b) that is
well-separated from the resistive superficial unit – continuing on the right
– by a clear conductive formation (very differently from what is retrieved
by the CDI). In addition, the SCI result shows interesting resistive
features incised into the more conductive surroundings (in particular, see the two deepening structures located between 20 and 30 km – circled in
black in Fig. 4b). The considerable depth of
investigation (DOI – indicated as a white mask in Fig. 4b) is worth noting; generally, the
geophysical model parameters can be considered sensitive to the data down to
the considerable depth of
In order to proceed further with the geological interpretation of the
geophysical model, the SCI result was gridded (Fig. 5). The general
signature trends visible in such a resistivity grid can be summarized as
follows:
areas with low resistivity values characterize argillaceous
layers in both the Bombouaka and Oti groups; sandstones have characteristically high resistivity values, with
the massive quartzose sandstones of the Bombouaka group (specifically, the
Panabako sandstone, Anyaboni sandstone, and upper Damongo formation)
displaying the lowest conductivities (Fugro Airborne Surveys Interpretation,
2009b).
Figure 6 shows the 3D geological model of the study area. It is mainly based
on the geophysics and the other source of available information (regolith
outlines from previous radiometric survey; Geological Survey Department,
2006). The developed geological model (consisting of 17.5 million
Figure 6 shows nine distinct stratigraphic units in the study area. These
include the following: (i) Bunya (youngest), (ii) Bimbila
In the study area, the Poubogou and Panabako formations of the Bombouaka group outcrop in the north. In contrast, outcrops of the basal unit of this group, the Tossiegou formation, have not been observed within the study area.
This is the oldest unit of the Bombouaka group identified by resistivity
signatures ranging between approximately 30 and 120
Cross section along SW–NE line 2 across the study area (see Fig. 1a for the location) with the conceptual geological interpretations showing the U-shaped valleys (between 53 and 63 km, whose location is indicated by three black arrows). In addition, two of the geologic logs (DWVP02 and DWVP01; Fig. 1a) used for the demarcation of lithostratigraphic boundaries and the interpretation and/or verification of the geophysical model are also shown. The two solid grey lines at the bottom represent the DOIs.
This unit is identified within the Gambaga escarpment with an average
thickness of 170 m (Fig. 7). The basin-wide distribution of this sequence
indicates a possible regional transgression event (Fugro Airborne Surveys
Limited, 2009b). The formation consists of green–grey micaceous mudstones
and siltstones intercalated with sandstones at some places. As it grades
into the overlying Panabako formation, there is an increase in the sandstone
proportion relative to the argillaceous beds (Carney et al., 2010). This
formation exhibits low resistivity in AEM profiles ranging between 0 and 20
This is a quartz-arenite-rich formation with a suggested thickness of
150–200 m (Carney et al., 2010). Lithostratigraphic mapping by Ayite et al. (2008) identified two subdivisions of the Panabako formation within the
Nakpanduri escarpment. The upper division consists of a nearshore aeolian
sequence, while the lower sequence is composed of a nearshore fluvial
sequence (lower Nakpanduri sandstone formation); Carney et al. (2010) correlate
the lower Nakpanduri with the upper Poubogou. From the current AEM data, this
subdivision is, however, observed entirely within the Panabako with the
presence of a distinct resistivity contrast clearly visible in the newly
inverted data (e.g., Fig. 7); indeed, in the new AEM reconstruction, the
upper Panabako shows higher resistivities – ranging from approximately 60 to
200
This group underlies the southern portion of the study area. Generally, it records the transition from a shallow marine environment adjacent to a rifted margin into a marine foreland basin sequence represented by interbedded argillaceous and immature arenaceous material (Carney et al., 2010).
Composed of what is commonly known as the triad, this formation constitutes the basal unit of the Oti group (Fig. 6). Commonly, the Kodjari formation comprises (i) basal tillites, followed by (ii) a cap-carbonate limestone, and finally covered with (iii) laminated tuffs and ash-rich siltstones (Carney et al., 2010).
The presence of the Kodjari formation is not easily seen but can be inferred from the SCI resistivity sections by moderately resistive strata observed immediately above the topmost units of the Bombouaka group (Fig. 7). An average thickness of 75 m can be retrieved; however, it should be noted that its continuity throughout the basin has not been verified. Carney et al. (2010) noted that, at some localities, in the north of the Volta Basin, the overlying tuffaceous material of the Kodjari triad is seen to unconformably lie directly on the Panabako rocks of the Bombouaka as a result of the lateral discontinuity of these units. These occurrences are also confirmed in the reprocessed AEM data (e.g., Fig. 7, at around 40–45 km).
The Bimbila formation has two sandstone beds forming its upper and lower boundaries. These are the Chereponi sandstone member, which forms the basal stratum of the formation, and the Bunya sandstone member, which generates the exposed upper portion of the formation. The Bunya sandstone is observed as a moderately conductive layer in the AEM cross section, above the argillaceous material of the Bimbila (Fig. 7).
The argillaceous units of the formation consist mainly of green to khaki micaceous laminated mudstones, siltstones, and sandstones representing a continuation of foreland basin deposition.
The new results from the inversion of the AEM data reveal some amount of
deformation within the basin. Dips of approximately 20
Along NE–SW line 7 (Fig. 8), a vertical displacement is observed and is interpreted as a fault within the Bimbila. It aligns well (sensu lato) with late brittle faults (Crowe and Jackson-Hicks, 2008).
Cross section along NW–SE line 7 (Fig. 1a) showing faulting (within the red circle) in the Bimbila. The DOI is shown as a solid grey line (there are two of them according to their definition – more details on this distinction can be found in Christiansen and Auken, 2012).
An angular unconformity marking the transition between rocks of the Bombouaka (which began, according to Carney et al., 2010, to accumulate after 1000 Ma) and the Oti rocks (which have a maximum depositional age of 635 Ma; Carney et al., 2010) is observed in Fig. 7. The unconformity could possibly be related to the absence of zircons aged 950–600 Ma recorded by Kalsbeek et al. (2008), suggesting the presence of an oceanic gap that prevented the deposition of sediments. The unconformity separates continental deposits of the Bombouaka below from passive margin deposits of the Oti above (Kalsbeek et al., 2008).
Three characteristic U-shaped valleys towards the north of the basin, which are between
Horizontal resistivity slices at 60 m
The proposed presence of valleys between the upper and lower Panabako
sequences represents an unconformity before the deposition of the upper
Panabako sequence (Fig. 7). The geometry of the valleys, with their U-shaped
cross-sectional nature, leads to the deduction that glaciation could have played a
role in their formation. Moreover, new insights into the stratigraphy may be
implied by the possible presence of these valleys within the Panabako
formation. The high-energy event responsible for producing the
intra-formational unconformity most likely occurred within the wide age
range of
On the other hand, Deynoux et al. (2006) mention that the 400–500 m thick
glacially influenced succession was controlled by the tectonic evolution of
the nearby Pan-African belt with deposition at around 660 Ma. These proposed
pre-Marinoan, or possibly Sturtian (
The rocks of the Bombouaka group in the Voltaian sedimentary basin are said
to be reminiscent of the rocks in portions of Supergroup 1 in the
Taoudéni Basin (Shields-Zhou et
al., 2011), and the presence of glacial signatures in both groups suggests
that the pre-Marinoan glaciation must have been regional. The trends of the
paleovalleys in the study area, i.e., NW–SE (Fig. 9), align well with
paleogeographic reconstructions of glaciation in the NW Africa region, which
suggests the presence of an ice sheet towards the north of the Reguibat
shield with inferred glacial movement southwards towards the Pan-African
belt (Shields-Zhou et al., 2011). The glacial movement is further verified by
the transition of sediments in the region from glacial to a mixture of
glacial and marine and finally marine towards the border with the rocks of
the Pan-African belt. Some authors consider the combination of Sturtian
and Marinoan glaciations – both of the Cryogenian – to suggest a complete
glaciation event; i.e., the Snowball Earth, according to which both continental and oceanic
surfaces were covered by ice (Goddéris et al., 2003;
Hoffman and Li, 2009; MacGabhann, 2005). A possible point of contention for
the Snowball Earth hypothesis – that clearly suggests the ubiquitous
presence of ice sheets during the Marinoan and Sturtian (including the
warmest parts proximal to the paleo Equator as well as the
higher paleolatitudes and high elevations) – is the lack of such glacial
evidence in the West African craton from the Sturtian. In fact, within the
framework of that hypothesis, quite complex assumptions (Hoffman and Li,
2009) have been made in an attempt to justify the absence of glacial traces
in the West African craton located, during the Sturtian, in cold regions
(latitude around 60
To summarize, the geological interpretation of the newly reprocessed AEM data (with their significantly enhanced information content) facilitated the discovery of evidence showing the presence of potential paleovalleys, possibly acting as groundwater reservoirs. At the same time, the existence of such geological features and, in particular, their stratigraphic location within the bounds of the Panabako formation suggest the need for a possible revision of the stratigraphy of the Bombouaka group, especially within the study area. Furthermore, the new insights suggest that there was some pre-Marinoan glacial activity responsible for the paleovalleys within the Panabako (Hoffman and Li, 2009) in the Voltaian sedimentary basin; thus, this activity precedes the Marinoan glaciation episode that is generally associated with the Kodjari formation of the basin (Deynoux et al., 2006) but still occurs within the Cryogenian period. This new glaciation suggests the possibility of a Sturtian event, but this assertion is currently hypothetical and would need further investigations to verify. Possible glacial incisions within the Panabako seem reasonable because of the high paleolatitude of the West African craton but, at the same time, avoid the need for additional complex justifications for the absence of indications of ice sheets in poleward continents. Hence, the proposed combination of the Marinoan and Sturtian events in the Neoproterozoic Voltaian sedimentary basin, if verified, would be compatible with the hypothesis of a global Neoproterozoic Snowball Earth even at high paleolatitudes (Hoffman and Schrag, 2002).
The 3D geological model developed in this research is to be used as the basis for conceptualizing the hydrogeological context of the basin and the larger Voltaian supergroup. For instance, the apparent detection of valleys within the Panabako formation may provide an indication of a deeper, prolific aquifer system that has not been noted before in the hydrogeology of the Voltaian supergroup. The presence of such systems in the Voltaian would have significant implications for the large-scale development of groundwater resources for irrigation and other income-generating ventures in the area. The Voltaian supergroup has been noted as a difficult terrain in terms of groundwater resources development, and the Nasia Basin, in particular, is one of the basins where high borehole failure rates have led to chronic domestic water access challenges over several years. Within or after the current DANIDA project, the paleovalleys need to be further investigated, which will lead to both seismic surveys and the drilling of much deeper boreholes penetrating them.
The present research investigates the concrete possibility of using preexisting airborne electromagnetic data, originally collected for mineral exploration, to build accurate 3D geological models for hydrogeological purposes. The use of this specific kind of data (B-field time-domain electromagnetic measurements) for this scope is quite novel per se and, in this specific case, allowed for the reconstruction of the stratigraphy of the Nasia Basin within the Voltaian sedimentary basin. In particular, the proposed geo-modeling strategy made it possible to infer the presence of paleovalleys that have been identified as pre-Marinoan and may be products of a glaciation event within the Sturtian (old Cryogenian). The valleys correlate with glacial deposits observed in the Wassangara group of the Taoudéni Basin. This group is found within Supergroup 1, which correlates with the Bombouaka rocks of the Voltaian basin. If confirmed, the stratigraphic location of these potential paleovalleys within the Panabako formation would lead to a possible revision of the stratigraphy of the Bombouaka group, especially within the study area. Moreover, together, the paleovalleys and the glacial deposits give further evidence for a Snowball Earth event that possibly covered the entire Earth during the Neoproterozoic. So, the impact of these findings goes beyond the discovery of potential groundwater reservoirs (which by itself is extremely relevant from a socioeconomic perspective) and can contribute to a rethinking of the stratigraphy of the region and confirm the Neoproterozoic Snowball Earth hypothesis.
The rights to the data used in this research are owned by Geological Survey Authority of Ghana. The authors therefore do not have the right to make the data available to the public.
EAD contributed to investigation, data curation, methodology, visualization, writing the original draft, and review and editing. GV contributed to conceptualization, funding acquisition, investigation, data curation, methodology, software and algorithm development, supervision, validation, and review and editing. FJ contributed to investigation, methodology, supervision, validation, and review and editing. SMY contributed to conceptualization, funding acquisition, project administration, supervision, validation, and review and editing. BBY provided supervision.
The authors declare that they have no conflict of interest.
The authors would like to thank DANIDA for its support of this research through the South-driven project “Ground Water Development and Sustainable Agriculture (proj. code: 14-P02-GHA)”, also known as “GhanAqua”, and the Geological Survey Authority of Ghana for providing most of the data and for its invaluable help. In this respect, special thanks go to Emmanuel Mensah and the director, John Agyei Duodu. In addition, the authors are very grateful to Kurt Klitten and Per Kalvig from the Geological Survey of Denmark and Greenland for their love of Ghana and for making this adventure possible.
This research has been supported by the DANIDA Fellowship Centre (grant no. 14-P02-GHA).
This paper was edited by Ulrike Werban and reviewed by Richard Smith, Giorgio Ghiglieri, and one anonymous referee.