# Country-scale lithogeochemical interpretation of stream sediment dataset in central Cameroon: Toward an integrated tool to support national geophysics surveys and geological mapping programs

Guillaume Vic, Julien Bernard, Matthieu Chevillard, Laurent Bailly, Constantin Maurice Ndongue, Yann Itard, Jérémie Melleton, Guillaume Martelet, Etienne Kouokam, Eric Fournier, et al.

► To cite this version:

Guillaume Vic, Julien Bernard, Matthieu Chevillard, Laurent Bailly, Constantin Maurice Ndongue, et al. Country-scale lithogeochemical interpretation of stream sediment dataset in central Cameroon: Toward an integrated tool to support national geophysics surveys and geological mapping programs. Applied Geochemistry, 2025, 183, pp.106321. 10.1016/j.apgeochem.2025.106321. hal-04978068

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# Country-scale lithogeochemical interpretation of stream sediment dataset in central Cameroon: Toward an integrated tool to support national geophysics surveys and geological mapping programs

G. Vic<sup>a,*</sup>, J. Bernard<sup>a</sup>, M. Chevillard<sup>a</sup>, L. Bailly<sup>a</sup>, C. Ndongue<sup>b</sup>, Y. Itard<sup>c</sup>, J. Melleton<sup>a</sup>, G. Martelet<sup>a</sup>, E. Kouokam<sup>b</sup>, E. Fournier<sup>d</sup>, R. Couëffé<sup>a</sup>, D. Keutia<sup>b</sup>

## ARTICLE INFO

Editorial Handling by: Pablo Pasten

Keywords:

Stream sediment geochemistry

Country scale

Lithogeochemical signatures

Compositional data analysis

Principal component analysis

Airborne gamma ray spectrometry

Geological interpretation

Cameroon

## ABSTRACT

In the frame of a 1:200,000 mapping project of Cameroon coordinated by PRECASEM for the Ministry of Mine, Industry and Technological Development (MINMITD), between 2016 and 2021, and carried out by BRGM-GTK-BEIG3 Consortium, a dataset of nearly 15,500 stream sediment samples were collected, at an average sampling density of 1 sample per 10 km², over an area of some 157,000 km², and analysed for 49 major and traces elements plus gold. The first aim of the geochemical survey was to identify, at the country scale, metallic anomalies and districts to promote the mining potential of Cameroon to mining investors.

In order to expand the use of this unique geochemical dataset, multivariate geostatistical processing by Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) was performed for lithogeochemical purpose. This processing enabled the identification of several lithogeochemical signatures which match very well with the main geological domains and formations obtained by conventional geological field survey. Moreover, in areas covered by thick lateritic profile like in the Adamawa-Yadé subdomain, the geostatistical processing was able to identify the granitic nature of the bedrock.

Secondly, the geochemical results obtained for U, K and Th on stream sediments were handled by inverse distance interpolation (IDW), and represented in a ternary combination in the RGB colour space to generate a pseudo-radiometric map to be compared with the high-resolution regional-scale airborne geophysical survey (magnetic and radiometric data) undertaken prior to the beginning of the mapping project. Similarities between both maps are remarkable. Moreover, the pseudo-radiometric map appears also more discriminant in areas covered by thick forest and thick lateritic profile, in humid and swampy environments allowing the identification of structural features and geochemical contrasts unrevealed by the airborne survey.

This contribution shows that stream sediment regional-to country-scale dataset should not only be used to highlight the mining potential of the studied area, but also represent a powerful tool, once derived in lithogeochemical and pseudo-radiometric interpretative maps, which can be efficiently used to decipher the major geological contrasts and structural feature of the country rocks. This information is of primary importance to define and calibrate the targets, logistics and costs of the geological field survey classically associated with these national programs, especially in areas with poor outcrops and/or not covered by airborne geophysical surveys.

## 1. Introduction

One of the main objectives of a geochemical survey on stream sediments is to understand the lithogeochemistry of the country rocks in

order to identify and highlight metallic anomalies, whether on a regional scale or for mining target characterization (Butt, 1987; Butt and Zeegers, 1989; Taylor et al., 1989; Barbier, 1987; Fletcher, 1997; Chandrajith et al., 2001; Ferreira et al., 2001; Naseem et al., 2002; Xie

<footnote><sup>a</sup> BRGM, F-45060, Orléans, France</footnote>

<footnote><sup>b</sup> BEIG3, Zone MAGZI Sud Yaoundé Mvan, BP11792, Yaoundé, Cameroon</footnote>

<footnote><sup>c</sup> Yann Itard Consultant, 72500, Jupilles, France</footnote>

<footnote><sup>d</sup> Newmont LaSource, 549C Route de Suzini, 97354, Rémire-Montjoly, Guyana</footnote>

<footnote>* Corresponding author.
E-mail address: g.vic@brgm.fr (G. Vic).</footnote>
et al., 2008; Salminen et al., 2008; Zuo et al., 2009, 2019; Lapworth
et al., 2012; Hao et al., 2014; Zhao et al., 2014; Zhizhong et al., 2014;
Caritat, 2018; Vicente et al., 2021; Eskdale et al., 2023), with an
increasing and promising use of emerging supervised machine learning
methods (Wang et al., 2020; Ge et al., 2022).

Most historical exploration campaigns were focused on limited geochemical analyses, performed on selected sets of elements, mainly base and precious metals, or key pathfinder elements (As, Sb, Se, Tl) depending on the interpreted metallogenic context of the investigated area and on the prospected resources. Nowadays, analytical developments, lowering of detection limits and decreases in analytical costs allow to routinely analyse more than 50 chemical elements in stream sediment batches, offering many new developments and applications including the definition of geochemical background for environmental purposes (Lapworth et al., 2012; Kirkwood et al., 2016; Salomoa et al., 2019) or the impact of the regolith on the stream sediments geochemical signature and its consequences on mining exploration or geological mapping (Tardy and Nahon, 1985; Butt, 1987; Grunsky et al., 2017).

Studies aiming at supporting geological mapping with stream sedi-
ment geochemical data begin to emerge (e.g. Itard and Labbé, 2016).
Kirkwood et al. (2016) introduce a multivariate analysis on stream
sediments in Southwest England, in order to demonstrate the relevance
of such datasets to determine the main lithologies of the underlying
bedrock. On the same note, another multivariate approach was used by
Grunsky et al. (2014) on lake sediments in Canada and by Grunsky et al.
(2017) on composite samples of soils, regolith and stream sediments
collected by the NGSA (National Geochemical Survey of Australia).

In this contribution, we propose to display stream sediment geochemical data on a GIS-based refined cartographic representation of the main statistical Principal Component Analysis (PCA) through Agglomerative Hierarchical Clustering (AHC), in order to decipher key characteristics of the lithogeochemical signatures of the underlying geological bedrock units at the country-scale. We use a dataset of some 15,500 stream sediment samples collected at a density of ~1/10 km² in Cameroon between 2016 and 2018 as part of the PRECASEM project aiming at updating the geological knowledge and mining potential of the country. This dataset covers roughly 157,000 km² between 4° and 9° North latitude and 10° and 14° East longitude. It represents among the largest country-scale stream sediment geochemical survey performed in central Africa for which data were collected, analysed and interpreted in a single coherent process. The wide diversity of the geological background in the studied area, consisting of a polygenetic Precambrian basement covered by Meso-to Cenozoic sedimentary deposits and volcanics, provides an ideal environment to evaluate the potential of stream sediment geochemical datasets to be routinely used as an input layer in classical country-scale geological mapping programs.

The objectives of this country-scale study are multiples. In addition to the classical identification of metallic geochemical anomalies, which is not described here, various geostatistical processings were performed on the geochemical dataset, seeking to (i) introduce a lithogeochemical interpretation based on factorial analyses and agglomerative hierarchical clustering; (ii) compare the U–K–Th airborne geophysics country-scale radiometric maps to the geochemical U–K–Th data; and finally (iii) demonstrate the interest of such approaches to support a geological mapping program.

## 2. Geological context of the study area

The geology of Cameroon is dominated by its Precambrian basement, structured during several geodynamic events and orogenic phases. It belongs to Central African Orogenic Belt (CAOB), located on the northern edge (current position) of the Congo-Saô Francisco craton, later on agglomerated to the West African and Sahara cratons or meta-cratons and having a transatlantic counterpart in Brazil (Rogers et al., 1995; Abdelsalam et al., 2002; Toteu et al., 2004; Oliveira et al., 2006;

De Wit et al., 2008; Ngako et al., 2008a,b; Van Schmus et al., 2008; Liégeois et al., 2013; Santos et al., 2017; Ganade et al., 2021). The CAOB consists of various geological domains - of continental or oceanic origin, derived from juvenile volcanic or continental magmatic arcs - accreted around the Archean cratons throughout the Neoproterozoic (≈700-550 Ma), during the Brasiliano-Panafrican orogeny, leading to the formation of the Gondwana supercontinent (Kennedy, 1964; Castaing et al., 1994; Rogers et al., 1995; Caby, 2003; Toteu et al., 2004; De Wit et al., 2008; Ngako et al., 2008a,b; Van Schmus et al., 2008; Neves et al., 2014; Santos et al., 2017). The extension of Phanerozoic rocks is limited, restricted to discordant Cretaceous sedimentary rocks deposited into the structurally-controlled basins of the WCARS (West and Central Africa Rift System; Fairhead, 1992; Binks and Fairhead, 1992; Guiraud et al., 1992); and to the volcano-plutonic products of the “Y-shaped” Cenozoic Cameroon Line (Fitton and Dunlop, 1985; Njonfang et al., 2011; Njome and de Wit, 2014, Fig. 1).

Panafrican accretionary processes isolated distinct lithospheric domains through the activation (or reactivation) of major tectonic structures, in particular ductile shear zones which concentrated most of the deformation. Previous studies agreed to subdivide Cameroon into four main geological domains (Nzenti et al., 1998; Toteu et al., 2001, 2004; Penaye et al., 2004; Van Schmus et al., 2008; Ngako et al., 2008a,b; Ngako and Njonfang, 2011 and references therein) which have been recently re-visited in the frame of the Cameroon PRECASEM project enabling the publication of the second edition of the geological map of the country at 1:1,000,000 scale (Delor et al., 2021). The extended Congo Craton Domain (Fig. 1) crops out in most of the south-eastern part of Cameroon. This domain is limited to the north by the sinistral Tcholiré-Banyo shear zone (TBSZ), early recognized as a major regional boundary (Pinna et al., 1994), and to the west by a succession of thrusts and shear zones in the strike of the Campo-Lolodorf and Bayomen (BTSZ) structures (Delor et al., 2021). The Bamenda-Tignère and Garoua Domains (Fig. 1) are both located north of the TBSZ main boundary and are separated from each other by the Poli Thrust System (PTS). The Nyong-Bayomen Domain is stuck between the TBSZ and the BTSZ in the south-western part of Cameroon (Fig. 1).

In the study area, the **extended Congo Craton Domain** is subdivided into two sub-domains. The **Yaoundé sub-domain** is mostly formed by micaschists, paragneisses, outstanding quartzite layers and often garnet- or kyanite-bearing gneisses affected by contrasted metamorphic grade, from green schist facies up to granulite, and frequently migmatitic (Nédélec et al., 1986; Nzenti et al., 1988; Barbey et al., 1990; Toteu et al., 2004, 2006b; Owona et al., 2011). These supracrustal volcano-sedimentary series were deposited in intracontinental rift or in a passive margin context (Nzenti et al., 1988; Ngnotué et al., 2000; Tchouatcha et al., 2018) at a widely debated age (Penaye et al., 1993; Toteu et al., 2006b; Ngnotué et al., 2012; Kalsbeek et al., 2013) which have recently been proposed to be Ectasian to Tonian (Fig. 1; Delor et al., 2021). They are intruded by plutonic rocks of different ages including the Tonian Ntui alkaline Suite (Fig. 1), and globally thrusted southwards during the Panafrican Cryogenian overlap (~620-580 Ma; Nzenti et al., 1988a, b, c; Penaye et al., 1993; Ngako et al., 2003, 2008; Mvondo et al., 2003; Toteu et al., 2004, 2006 a&b). Intra-basin thrusts (Fig. 1) may have accommodated most of the displacement, while low-grade supracrustal rocks could be partially sub-autochthonous (Delor et al., 2021). The **Adamawa-Yadé sub-domain** is on the contrary mainly formed by voluminous Suites and batches of Cryogenian to Ediacaran plutonic rocks emplaced at Panafrican times (Soba et al., 1991; Ganwa, 1998; Tchakounté, 1999; Toteu et al., 1994, 2001, 2004, 2006a; Tchaméni et al., 2000; Penaye et al., 2004; van Schmus et al., 2008; Nkoumbou et al., 2014; Ganwa et al., 2016; Negue et al., 2017; Saha-Fouotsa et al., 2019) into a heterogeneous Steno-Tonian migmatitic basement intensely sheared and remobilized during the Cryogenian (Delor et al., 2021), and with Archean and Paleoproterozoic imprints (Penaye et al., 2004; Nkoumbou et al., 2014; Tchakounté et al., 2017, 2018). The Adamawa-Yadé subdomain is cross-cutted by the Central Cameroon
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Simplified geological map at 1:500,000 scale of the studied area, modified from Delor et al. (2021). (A) Main country-scale geological Domains of Cameroon (B-T = Bamenda-Tignère; G = Garoua; N-B = Nyong-Bayomen; CC = Extended Congo Craton). (B) Main litho-stratigraphic units and regional structures of the study area (CL = Cameroon Line; K = Kontcha basin; Nk = Nkondjock basin; D-J = Djerem-Mbere basin; Nd = Ndikinimeki Group; L = Lom Group; P-B = Poli-Bibemi Group; Y = Yaoundé Group; J = Juvenile Suite; C = Crustal magmatism).</figcaption></figure>

shear zone (CCSZ; Ngako et al., 2008a,b; Fig. 1) and includes the Lom metasedimentary and volcanoclastic Precambrian basin (Toteu et al., 2006b, Fig. 1). Both Yaoundé and Adamawa-Yadé sub-domains encloses Archean blocks (Ntal and Bindiba-Rinda complexes). Delor et al. (2021) interpreted the whole Domain as the maximal extension of the northern border of the Congo Craton, decratonized during an extensive Steno-Tonian rifting event (equivalent to the Cariris Velhos described in Brazil; dos Santos et al., 2010; Guimarães et al., 2012; Caxito et al.,

2020; Ganade et al., 2021 and references therein) and deeply reworked later on by the Panafrican orogeny.

The **Nyong-Bayomen Domain** is composed of strongly deformed, mostly ortho-derived, high-grade gneisses pinched between southwest-northeast striking and vertically dipping shear zones (Toteu et al., 1994, 2001; Lerouge et al., 2006; Delor et al., 2021). The Nyong-Bayomen Domain also comprises the Ndikinimeki Group which represents a highly deformed and migmatitized Neoproterozoic
supracrustal volcano-sedimentary cover thrusted toward the south-south-east during the Panafrican orogeny (Tchakounté et al., 2007, 2017; Delor et al., 2021). Granitoïds of the Panafrican Suites intrudes both sets of rocks, especially in the region north of the BSZ (Fig. 1; Delor et al., 2021). Scarce igneous rocks of Paleoproterozoic “Eburnean” age are also present (Lerouge et al., 2006; Delor et al., 2021). The Nyong-Bayomen Domain is interpreted as a slice of the Archean Congo Craton which have repetitively been deeply metamorphosed, reworked and transposed to the northeast, both at the Eburnean and Panafrican times (Delor et al., 2021).

The **Bamenda-Tignère** (formerly “North-West”) Domain was historically described as a juvenile block mainly composed of calc-alkaline Panafrican granitoids devoid (or slightly contaminated) of Archean and/or Paleoproterozoic basement (Toteu et al., 2004; Nzenti et al., 2006; Ngako et al., 2008a,b; Bouyo et al., 2009 & 2013). Recent works confirmed the strong impact of the Panafrican orogeny and magmatism on this Domain (Fig. 1) but also identified an ubiquitous ortho-derived migmatitic TTG basement of Paleoproterozoic age which isotopic Lu-Hf signature (strongly positive $\varepsilon_{Hf}$ value) is characteristic of a juvenile affinity (Delor et al., 2021). The Bamenda-Tignère Domain is thus probably a juvenile Paleoproterozoic arc or slab connected to Nigerian terranes and/or to the enigmatic Sahara Metacraton (Abdelsalam et al.,

2002; Liégeois et al., 2013), and accreted to the extended Congo Craton Domain during the Panafrican orogeny (Delor et al., 2021).

The **Garoua Domain** marginally crops out in the extreme northwest portion of the study area where it is composed of Neoproterozoic volcano-sedimentary sequence of high-grade schists and gneisses of the Poli-Bibemi Group (Toteu et al., 1987; Pinna et al., 1994; Bouyo et al., 2013; Delor et al., 2021) related to the Sinassi-Mayo Kebi magmatic arc (Bouyo et al., 2016).

### 3. Methodology

#### a. Stream sediment sampling plan

The provisional geochemical stream sediment sampling plan was established prior to the campaign for the 13.5 sheets at 1:200,000-scale of the entire study area (Fig. 2). Each sampling point was assigned to a watercourse to ensure that each sample represents a single watershed, with an area between 5 and 15 km². No sample was taken downstream of another in the same watercourse. Out of 15,811 sampling points initially positioned, 15,463 samples were collected over a total calculated sampling area of 157,709 km², representing a density of circa 1 sample/10 km² (Fig. 2).

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Location of the 15,463 stream sediment samples collected as part of the project. The insert displays the location of the 13.5 maps at 1: 200,000-scale of Cameroon.</figcaption></figure>
## b. Field sampling procedures

The methodological tests carried out before the sampling campaign showed that the grainsize fraction for which the geochemical signal is the strongest, ensuring the best contrast between regional geochemical background and metallic anomalies was carried by the finest <63 µm granulometric fraction, which was then selected for the subsequent analyses, following classical recommendations for such material (e.g. Salminen and Tarvainen, 1997; Itard and Labbé, 2016). In order to increase the representativeness of the sampled material, each sample represents a composite made of at least 4 sampling points collected only in the finest fraction of the most recently deposited sediment within the live stream bed. After drying, pounding and sieving, the <63 µm fraction was quartered in two sub-samples of approximately 100 g, one for geochemical analysis and the other kept as a reference. The 15,463 samples were analysed by the certified ALS Mineral Geochemistry laboratory in Ireland (Table 1).

In terms of QA/QC, 173 blanks (1.12 %), 370 laboratory duplicates (2.4 %) and 325 certified standards (2 %) were randomly added to the different sample batches and later analysed together with other samples in the same procedure to evaluate the quality of the final geochemical dataset.

48 major and trace elements were analysed after four acid digestion (HF-HCl-HNO₃-HClO₄) by ICP-AES/ICP-MS assay on 0.25 g of sample (ALS Method ME-MS61). Gold was analysed after aqua regia digestion and ICP-MS assay on 25 g of sample (ALS Method Au-ST43). The lower and higher detection limit of each elements analysed are given in Table 1.

## c. Dataset quality and filtering

Chemical elements of the analysed samples with analytical values below the lower limit of detection (LLD) have been replaced by generic values corresponding to half the detection limit (1/2 LLD) whereas analyses above the higher limit of detection (HLD) have been replaced by HLD + 1 unit. This approach, commonly used (Itard and Labbé, 2016; Kirkwood et al., 2016), allows statistical calculations to be carried out on the entire dataset without erratic non-numerical values distorting the results.

The descriptive statistics performed on the 15,463 samples and 49 elements (Table 2) combined with box plot representation led to the identification of 27 samples characterized by extreme or anomalous values in several elements (Bi, Co, Cr, Cu, Hf, REE, Li, Mo, Nb, Ni, Sb, Sr, Th, V, W, etc.). Such anomalous samples are known to alter global multivariate statistical analysis and were thus excluded from the dataset (Howarth, 1984; Weber and Davis, 1990; Cannon et al., 2004; Itard and Labbé, 2016).

Elements such as Au, Ag, Cd, In, Re, S, Se and Te show many values below LLD or above HLD. They are moreover rarely good lithological markers but rather associated with metallogenic processes (hydrothermal circulation, alteration, etc.). Since the main objective of this study is focused on litho geochemistry (i.e. on the underlying geological background), these elements were excluded from the multivariate statistical analysis. In addition, the correlation matrix calculated on the whole dataset highlights also the redundancy between some variables and isolates highly correlated elements (see Table A-1 in the Supplementary Material). This is the case for Ce, Ga and Zr respectively correlated with La (similarity of 0.94), Al (0.93) and Hf (0.944).

Taking into account all of these considerations and filters, the 38 chemical elements later used in our contribution as variables for the multivariate statistical analysis processes are: Al, As, Ba, Be, Bi, Ca, Co, Cr, Cs, Cu, Fe, Ge, Hf, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Sb, Sc, Sn, Sr, Ta, Th, Ti, Tl, U, V, W, Y and Zn.

# 4. Results

a 1:500,000-scale geochemical mapping

## 4.1. Principal Component Analysis (PCA)

The PCA is a method that is classically used to reduce the dimensionality of large datasets in order to make them more interpretable, e.g. representing it as vectors in a 2-dimensional correlation circle, while maintaining most of its variability (Pearson, 1901; Hotelling, 1933). It consists in creating new variables decorrelated one from each other, called principal components (PC), which are linear functions of the variables of the original dataset (Jolliffe and Cadima, 2016). This approach is widely used to process geochemical dataset (e.g. Zuo et al., 2021). Prior to the PCA analysis, a linear reduction filter was applied to the raw geochemical data in order to obtain centered variables with a reduced effect of scale, and to eliminates the weight and influence of extreme variables in order to process them equally, regardless of their raw % or ppm ranges of values.

The PCA was performed on the 38 elements selected after the filtering and quality steps previously described. Each components calculated by PCA represent a statistical measure of the dispersion of the points (i.e. geochemical elements) characterized by their “eigenvalue”. The variability is the same measure, standardized at 100 %, while the total of the eigenvalues is equal to the number of variables used, 38 in this study (see Figure A-1 in the Supplementary Material). The first eight principal components (PC1, PC2, PC3, PC4, PC5, PC6, PC7 and PC8)

<table><caption>**Table 1**

Detail of the 49 elements analysed in multi-elementary geochemistry. LLD = lower limit of detection; HLD = higher limit of detection.</caption><thead><tr><th>ELEMENT</th><th>UNIT</th><th>LLD</th><th>HLD</th><th>ELEMENT</th><th>UNIT</th><th>LLD</th><th>HLD</th><th>ELEMENT</th><th>UNIT</th><th>LLD</th><th>HLD</th></tr></thead><tbody><tr><td>Ag</td><td>ppm</td><td>0,01</td><td>100</td><td>Hf</td><td>ppm</td><td>0,1</td><td>500</td><td>Sb</td><td>ppm</td><td>0,05</td><td>10,000</td></tr><tr><td>Al</td><td>%</td><td>0,01</td><td>50</td><td>In</td><td>ppm</td><td>0,005</td><td>500</td><td>Sc</td><td>ppm</td><td>0,1</td><td>10,000</td></tr><tr><td>As</td><td>ppm</td><td>0,2</td><td>10,000</td><td>K</td><td>%</td><td>0,01</td><td>10</td><td>Se</td><td>ppm</td><td>1</td><td>1000</td></tr><tr><td>Au</td><td>ppb</td><td>0,1</td><td>100</td><td>La</td><td>ppm</td><td>0,5</td><td>10,000</td><td>Sn</td><td>ppm</td><td>0,2</td><td>500</td></tr><tr><td>Ba</td><td>ppm</td><td>10</td><td>10,000</td><td>Li</td><td>ppm</td><td>0,2</td><td>10,000</td><td>Sr</td><td>ppm</td><td>0,2</td><td>10,000</td></tr><tr><td>Be</td><td>ppm</td><td>0,05</td><td>1000</td><td>Mg</td><td>%</td><td>0,01</td><td>50</td><td>Ta</td><td>ppm</td><td>0,05</td><td>100</td></tr><tr><td>Bi</td><td>ppm</td><td>0,01</td><td>10,000</td><td>Mn</td><td>ppm</td><td>5</td><td>100,000</td><td>Te</td><td>ppm</td><td>0,05</td><td>500</td></tr><tr><td>Ca</td><td>%</td><td>0,01</td><td>50</td><td>Mo</td><td>ppm</td><td>0,05</td><td>10,000</td><td>Th</td><td>ppm</td><td>0,01</td><td>10,000</td></tr><tr><td>Cd</td><td>ppm</td><td>0,02</td><td>1000</td><td>Na</td><td>%</td><td>0,01</td><td>10</td><td>Ti</td><td>%</td><td>0,005</td><td>10</td></tr><tr><td>Ce</td><td>ppm</td><td>0,01</td><td>500</td><td>Nb</td><td>ppm</td><td>0,1</td><td>500</td><td>Tl</td><td>ppm</td><td>0,02</td><td>10,000</td></tr><tr><td>Co</td><td>ppm</td><td>0,1</td><td>10,000</td><td>Ni</td><td>ppm</td><td>0,2</td><td>10,000</td><td>U</td><td>ppm</td><td>0,1</td><td>10,000</td></tr><tr><td>Cr</td><td>ppm</td><td>1</td><td>10,000</td><td>P</td><td>ppm</td><td>10</td><td>10,000</td><td>V</td><td>ppm</td><td>1</td><td>10,000</td></tr><tr><td>Cs</td><td>ppm</td><td>0,05</td><td>500</td><td>Pb</td><td>ppm</td><td>0,5</td><td>10,000</td><td>W</td><td>ppm</td><td>0,1</td><td>10,000</td></tr><tr><td>Cu</td><td>ppm</td><td>0,2</td><td>10,000</td><td>Rb</td><td>ppm</td><td>0,1</td><td>10,000</td><td>Y</td><td>ppm</td><td>0,1</td><td>500</td></tr><tr><td>Fe</td><td>%</td><td>0,01</td><td>50</td><td>Re</td><td>ppm</td><td>0,002</td><td>50</td><td>Zn</td><td>ppm</td><td>2</td><td>10,000</td></tr><tr><td>Ga</td><td>ppm</td><td>0,05</td><td>10,000</td><td>S</td><td>%</td><td>0,01</td><td>10</td><td>Zr</td><td>ppm</td><td>0,5</td><td>500</td></tr><tr><td>Ge</td><td>ppm</td><td>0,05</td><td>500</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<caption>**Table 2**</caption>

Summary of descriptive statistics of the 15,463 samples analysed for the 49 variables. SMP = Sample; Var = Variance; CV = Coefficient of variation; SD = Standard deviation.

<table><thead><tr><th>Element</th><th>SMP</th><th>Min</th><th>Max</th><th>1st Quartile</th><th>Median</th><th>3rd Quartile</th><th>Mean</th><th>Var</th><th>SD</th><th>CV</th></tr></thead><tbody><tr><td>Ag (ppm)</td><td>15,463</td><td>0,005</td><td>4,77</td><td>0,030</td><td>0,040</td><td>0,060</td><td>0,046</td><td>0,003</td><td>0,053</td><td>1158</td></tr><tr><td>Al (%)</td><td>15,463</td><td>1270</td><td>14,95</td><td>6825</td><td>8250</td><td>9930</td><td>8353</td><td>4790</td><td>2189</td><td>0,262</td></tr><tr><td>As (ppm)</td><td>15,463</td><td>0,100</td><td>167,00</td><td>0,700</td><td>1200</td><td>2000</td><td>1711</td><td>12,829</td><td>3582</td><td>2093</td></tr><tr><td>Ba (ppm)</td><td>15,463</td><td>40,000</td><td>4620,00</td><td>340,000</td><td>620,000</td><td>990,000</td><td>734,552</td><td>264,659,661</td><td>514,451</td><td>0,700</td></tr><tr><td>Be (ppm)</td><td>15,463</td><td>0,320</td><td>13,60</td><td>1430</td><td>2000</td><td>2680</td><td>2157</td><td>1084</td><td>1041</td><td>0,483</td></tr><tr><td>Bi (ppm)</td><td>15,463</td><td>0,005</td><td>2,98</td><td>0,090</td><td>0,140</td><td>0,200</td><td>0,169</td><td>0,018</td><td>0,135</td><td>0,797</td></tr><tr><td>Ca (%)</td><td>15,463</td><td>0,010</td><td>3,75</td><td>0,110</td><td>0,180</td><td>0,330</td><td>0,310</td><td>0,139</td><td>0,372</td><td>1203</td></tr><tr><td>Cd (ppm)</td><td>15,463</td><td>0,010</td><td>0,71</td><td>0,030</td><td>0,060</td><td>0,110</td><td>0,082</td><td>0,004</td><td>0,067</td><td>0,810</td></tr><tr><td>Ce (ppm)</td><td>15,463</td><td>18,800</td><td>501,00</td><td>102,500</td><td>136,500</td><td>175,500</td><td>146,433</td><td>4551,691</td><td>67,466</td><td>0,461</td></tr><tr><td>Co (ppm)</td><td>15,463</td><td>2000</td><td>265,00</td><td>9900</td><td>14,800</td><td>21,300</td><td>17,355</td><td>146,979</td><td>12,123</td><td>0,699</td></tr><tr><td>Cr (ppm)</td><td>15,463</td><td>1000</td><td>4910,00</td><td>54,000</td><td>71,000</td><td>95,000</td><td>82,782</td><td>4762,834</td><td>69,013</td><td>0,834</td></tr><tr><td>Cs (ppm)</td><td>15,463</td><td>0,270</td><td>16,55</td><td>1580</td><td>2260</td><td>3230</td><td>2568</td><td>2045</td><td>1430</td><td>0,557</td></tr><tr><td>Cu (ppm)</td><td>15,463</td><td>2900</td><td>281,00</td><td>17,400</td><td>25,200</td><td>35,900</td><td>28,284</td><td>230,500</td><td>15,182</td><td>0,537</td></tr><tr><td>Fe (%)</td><td>15,463</td><td>0,340</td><td>34,70</td><td>2240</td><td>3850</td><td>5520</td><td>4319</td><td>8688</td><td>2948</td><td>0,682</td></tr><tr><td>Ga (ppm)</td><td>15,463</td><td>3550</td><td>64,10</td><td>18,350</td><td>22,600</td><td>27,500</td><td>22,971</td><td>40,844</td><td>6391</td><td>0,278</td></tr><tr><td>Ge (ppm)</td><td>15,463</td><td>0,025</td><td>0,98</td><td>0,150</td><td>0,190</td><td>0,240</td><td>0,204</td><td>0,007</td><td>0,084</td><td>0,412</td></tr><tr><td>Hf (ppm)</td><td>15,463</td><td>0,400</td><td>62,70</td><td>3700</td><td>5500</td><td>7900</td><td>6295</td><td>13,358</td><td>3655</td><td>0,581</td></tr><tr><td>In (ppm)</td><td>15,463</td><td>0,011</td><td>0,34</td><td>0,059</td><td>0,073</td><td>0,087</td><td>0,075</td><td>0,001</td><td>0,024</td><td>0,315</td></tr><tr><td>K (%)</td><td>15,463</td><td>0,060</td><td>5,46</td><td>0,440</td><td>0,930</td><td>1690</td><td>1164</td><td>0,793</td><td>0,891</td><td>0,765</td></tr><tr><td>La (ppm)</td><td>15,463</td><td>10,000</td><td>540,00</td><td>47,400</td><td>61,300</td><td>78,400</td><td>66,842</td><td>992,413</td><td>31,503</td><td>0,471</td></tr><tr><td>Li (ppm)</td><td>15,463</td><td>1700</td><td>120,00</td><td>12,900</td><td>16,800</td><td>21,600</td><td>17,956</td><td>53,727</td><td>7330</td><td>0,408</td></tr><tr><td>Mg (%)</td><td>15,463</td><td>0,030</td><td>3,60</td><td>0,100</td><td>0,150</td><td>0,280</td><td>0,235</td><td>0,055</td><td>0,235</td><td>1002</td></tr><tr><td>Mn (ppm)</td><td>15,463</td><td>37,000</td><td>47,300,00</td><td>283,000</td><td>461,000</td><td>723,000</td><td>610,944</td><td>728,894,058</td><td>853,753</td><td>1397</td></tr><tr><td>Mo (ppm)</td><td>15,463</td><td>0,050</td><td>17,80</td><td>0,920</td><td>1250</td><td>1820</td><td>1471</td><td>0,708</td><td>0,842</td><td>0,572</td></tr><tr><td>Na (%)</td><td>15,463</td><td>0,005</td><td>2,68</td><td>0,050</td><td>0,110</td><td>0,250</td><td>0,224</td><td>0,091</td><td>0,302</td><td>1349</td></tr><tr><td>Nb (ppm)</td><td>15,463</td><td>2400</td><td>329,00</td><td>20,800</td><td>27,600</td><td>35,700</td><td>31,086</td><td>355,017</td><td>18,842</td><td>0,606</td></tr><tr><td>Ni (ppm)</td><td>15,463</td><td>4200</td><td>515,00</td><td>22,600</td><td>32,400</td><td>44,400</td><td>36,672</td><td>594,372</td><td>24,380</td><td>0,665</td></tr><tr><td>P (ppm)</td><td>15,463</td><td>130,000</td><td>5920,00</td><td>740,000</td><td>960,000</td><td>1220,000</td><td>1024,035</td><td>183,330,215</td><td>428,171</td><td>0,418</td></tr><tr><td>Pb (ppm)</td><td>15,463</td><td>2700</td><td>97,60</td><td>20,300</td><td>26,200</td><td>33,600</td><td>28,082</td><td>119,286</td><td>10,922</td><td>0,389</td></tr><tr><td>Rb (ppm)</td><td>15,463</td><td>3000</td><td>403,00</td><td>27,500</td><td>48,600</td><td>81,900</td><td>59,127</td><td>1709,546</td><td>41,347</td><td>0,699</td></tr><tr><td>Re (ppm)</td><td>15,463</td><td>0,000</td><td>0,02</td><td>0,001</td><td>0,001</td><td>0,001</td><td>0,001</td><td>0,000</td><td>0,001</td><td>0,625</td></tr><tr><td>S (%)</td><td>15,463</td><td>0,010</td><td>0,51</td><td>0,030</td><td>0,050</td><td>0,070</td><td>0,054</td><td>0,001</td><td>0,033</td><td>0,608</td></tr><tr><td>Sb (ppm)</td><td>15,463</td><td>0,025</td><td>35,60</td><td>0,110</td><td>0,160</td><td>0,210</td><td>0,190</td><td>0,287</td><td>0,535</td><td>2816</td></tr><tr><td>Sc (ppm)</td><td>15,463</td><td>2500</td><td>54,10</td><td>10,400</td><td>13,500</td><td>16,900</td><td>13,951</td><td>24,258</td><td>4925</td><td>0,353</td></tr><tr><td>Se (ppm)</td><td>15,463</td><td>0,500</td><td>5,00</td><td>1000</td><td>1000</td><td>1000</td><td>1092</td><td>0,317</td><td>0,563</td><td>0,516</td></tr><tr><td>Sn (ppm)</td><td>15,463</td><td>0,500</td><td>20,40</td><td>2300</td><td>2900</td><td>3600</td><td>3052</td><td>1457</td><td>1207</td><td>0,396</td></tr><tr><td>Sr (ppm)</td><td>15,463</td><td>11,300</td><td>1210,00</td><td>52,100</td><td>103,500</td><td>183,250</td><td>139,459</td><td>14,498,555</td><td>120,410</td><td>0,863</td></tr><tr><td>Ta (ppm)</td><td>15,463</td><td>0,180</td><td>21,50</td><td>1300</td><td>1780</td><td>2380</td><td>2004</td><td>1374</td><td>1172</td><td>0,585</td></tr><tr><td>Te (ppm)</td><td>15,463</td><td>0,020</td><td>0,43</td><td>0,025</td><td>0,025</td><td>0,050</td><td>0,037</td><td>0,001</td><td>0,023</td><td>0,617</td></tr><tr><td>Th (ppm)</td><td>15,463</td><td>2370</td><td>251,00</td><td>14,100</td><td>19,550</td><td>26,900</td><td>22,788</td><td>221,764</td><td>14,892</td><td>0,653</td></tr><tr><td>Ti (%)</td><td>15,463</td><td>0,070</td><td>5,83</td><td>0,639</td><td>0,777</td><td>0,951</td><td>0,852</td><td>0,149</td><td>0,386</td><td>0,453</td></tr><tr><td>Tl (ppm)</td><td>15,463</td><td>0,040</td><td>2,09</td><td>0,280</td><td>0,400</td><td>0,550</td><td>0,438</td><td>0,049</td><td>0,221</td><td>0,504</td></tr><tr><td>U (ppm)</td><td>15,463</td><td>0,500</td><td>47,80</td><td>2700</td><td>3700</td><td>5300</td><td>4382</td><td>8335</td><td>2887</td><td>0,659</td></tr><tr><td>V (ppm)</td><td>15,463</td><td>17,000</td><td>456,00</td><td>76,000</td><td>99,000</td><td>123,500</td><td>102,482</td><td>1483,639</td><td>38,518</td><td>0,376</td></tr><tr><td>W (ppm)</td><td>15,463</td><td>0,050</td><td>36,60</td><td>0,700</td><td>1000</td><td>1400</td><td>1191</td><td>0,724</td><td>0,851</td><td>0,714</td></tr><tr><td>Y (ppm)</td><td>15,463</td><td>4300</td><td>185,50</td><td>18,600</td><td>24,200</td><td>30,200</td><td>25,323</td><td>104,032</td><td>10,200</td><td>0,403</td></tr><tr><td>Zn (ppm)</td><td>15,463</td><td>12,000</td><td>403,00</td><td>47,000</td><td>63,000</td><td>81,000</td><td>66,223</td><td>698,006</td><td>26,420</td><td>0,399</td></tr><tr><td>Zr (ppm)</td><td>15,463</td><td>15,300</td><td>501,00</td><td>135,500</td><td>200,000</td><td>288,000</td><td>222,562</td><td>12,969,050</td><td>113,882</td><td>0,512</td></tr><tr><td>Au (ppb)</td><td>15,463</td><td>0,050</td><td>6350,00</td><td>0,300</td><td>0,500</td><td>0,900</td><td>1440</td><td>2650,987</td><td>51,488</td><td>35,754</td></tr></tbody></table>

represent almost 74 % of the total variability whereas all components after PC8 represent less than 3 % variability each (see Table A-2 in the Supplementary Material), which we consider to be negligible in terms of lithogeochemical representativity. The statistical analyses are therefore later focused on the first 8 components for which positive and negative signature, according to the coordinates of the variables, are given in Table 3 together with chemically-based a proposed rock-type signature detailed below.

An interpolated cartographic representation of each of these principal components was produced by an inverse distance weight (IDW) interpolation in order to better visualize the geochemical contrasts and compare it with the main geological feature at the 1: 500,000-scale (Fig. 3). A RGB representation of the principal components allows displaying it on 3 bands (out of the 8 available PCs), depending on the cartographic objectives, the lithological signature and the geological contrasts that we want to highlight. Fig. 3 shows examples of PCs displayed in the RGB color space and associated hues for PC1-PC2-PC3, PC3-PC7-PC6 and PC2-PC5-PC7 combinations, respectively. Comparison between our results and 1: 500,000-scale geological maps shows

<caption>**Table 3**</caption>

Summary of the associations of positive and negative poles for the first eight PCA principal components. Elements in brackets represent light signature.

<table><thead><tr><th>PC</th><th>Positive pole</th><th>Negative pole</th><th>Signature proposal</th></tr></thead><tbody><tr><td>PC1</td><td>Al, Bi, Cr, Cs, Cu, Fe, Ge, Li, Mo, Ni, P, Sc, Sn, V, W, Y (Be, Co, La, Nb, Pb, Ta, Ti, U, Zn)</td><td>Ca, Na, Sr</td><td>Basalt, syenite, monzonite, hyperalkaline granite</td></tr><tr><td>PC2</td><td>Ba, Be, Hf, K, Pb, Rb, Tl, U (Cs, Na, Sn, Th)</td><td>Co, Cr, Cu, Fe, Ni, P, Sc, Ti, V, Zn</td><td>Potassic leucogranite</td></tr><tr><td>PC3</td><td>Ba, Ca, Co, Mg, Na, Sr, Zn (Cu, Ge, K, Mn, Rb)</td><td>Bi, Hf, Nb, Sn, Ta, U, W</td><td>Calco-alkaline granite</td></tr><tr><td>PC4</td><td>La, Th (Ge, Hf, Y, Ce)</td><td>As, Bi, Ca, Cs, Li, Mg, Na, Sb, Sr, W</td><td>Anatectic granite, migmatite, heavy mineral sands</td></tr><tr><td>PC5</td><td>Nb, Ta, Ti (Na, Sr, Ba)</td><td>Cs, Li</td><td>Granite with rare metals, pegmatite, basalt</td></tr><tr><td>PC6</td><td>As, Sb (Mn)</td><td></td><td>Schists, Laterite</td></tr><tr><td>PC7</td><td>(Al)</td><td>Ca, Sb, Zn</td><td>Tonalite</td></tr><tr><td>PC8</td><td>Mn (Co, P)</td><td>La, Th</td><td>Mn/Co laterite</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Representation of various combinations of PCA principal components in the RGB color space. A: PC 1, 2 and 3; B: PC 3, 7 and 6; C: PC 2, 5 and 7; D: PC 1, 2 and 3 with the main regional structures and the geological boundaries are the same than Fig. 1. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

that the cartographic distribution of the PCs is consistent with the main lithological units found in the study area (Fig. 3).

- PC1 represents 19% of the total dataset variability and has a negative signature in Ca, Na and Sr (Table 3). PC1 encompasses all the late-Panafrican alkaline plutonic Suite as well as the Cenozoic Cameroon Volcano-Plutonic Line (Figs. 1 & 3D);

- PC2 reflects 16% of the dataset and is characterized by a positive signature in Be, K, Rb, U which matches with high-K and fractionated

leucogranite Suites and/or batholiths, mainly of Panafrican age (Figs. 1 & 3D);

- PC3 accounts for 15% of the variability and represents most of the large Panafrican calc-alkaline granitic suite domains of Bamenda-Tignère Domain and delineates also some geological boundaries in the Nyong-Bayomen and Yaoundé Domains or Sub-domains (Figs. 1 & 3D);

- PC4 represents 6% of the variability and groups altogether mafic-titic domains, associated anatectic granites, and locally, heavy mineral enrichments;
- PC5 reflects also 6% of the dataset and combines the delineation of rare metal-bearing alkaline syenitoid intrusions of the Ntui Suite in the Yaoundé Sub-domain as well as the most alkaline basaltic fields of the Cameroon Line (Figs. 1 & 3C);

- PC6 account for 4% of the dataset and clearly underlines the schist domains of the Lom and Poli sedimentary basins (Figs. 1 & 3B) with positive signature in As and Sb (Table 3);

- PC7 account also for 4% of the dataset and is characterized by a negative Ca, Sb, Zn signature cartographically compatible with the TTG migmatitic gneisses of the Paleoproterozoic and Archean relics of the Bamenda-Tignère, Nyong-Bayomen and Adamawa-Yadé (Bindiba-Rianda) Domains and Sub-domains (Figs. 1 & 3C);

- PC8 represents 4% of the variability and mostly highlights modest geological bodies of laterites enriched in Mn and Co.

Our results show that a first plot of the first 3 principal components (PC1, PC2 and PC3) representing a cumulative variability of 50 % is relevant to highlight the large calc-alkaline (blue), alkaline/basaltic (red) and granitic (green) main geological bodies of central Cameroon (Figs. 1 and 3A). Other representations using further components associations highlight more detailed signatures and/or local units. For example, the combination of PC3, PC7 and PC6 precisely maps the eastern volcano-sedimentary Neoproterozoic Lom basin in blue and the calc-alkaline affinity granitic domains in the north with a red hue (Fig. 3B). A combination of PC2, PC5 and PC7 discriminates basaltic flows, in bright green from alkaline/hyperalcaline granite, pegmatite and leucogranite whose shade vary between orange and yellow (Fig. 3C).

Fig. 3 also surprisingly demonstrate the ability of regional-scale stream sediment geochemical data to decipher the regional-scale structural network of the study area. Fig. 3C and D shows that most of the main regional-scale structures can clearly be delineated using geochemical data, including: Tcholliré-Banyo, Central Cameroon, Mbé-Meiganga, Bantoum, Bayomen, Ngoro-Belabo and Lom Shear zones drawn separately on the 1: 500,000-scale geological map by "classical" geological field-methods and airborne geophysical interpretations. This very good correlation between the two independent datasets (Fig. 3D) demonstrates the relevance of this mode of lithogeochemical multivariate statistical analysis representation as an additional tool for regional-scale geological mapping.

## 4.2. Agglomerative hierarchical clustering (AHC)

The agglomerative hierarchical clustering is an unsupervised classification method. Taking into account multiple variables, the AHC method allows the grouping of individual samples based on their resemblance, calculated through their Euclidean distance in the multi-variate space. Several standard aggregation criteria can be used to group individuals, such as the nearest neighbour or center of gravity. In the AHC algorithm that we use, the aggregation criterion is based on the variance (Rolet and Seguin, 1986), using the standard Ward formulation (Ward, 1963). An AHC was performed on the first 8 PCA principal components on the 15,436 samples. Again, the 27 anomalous samples were not considered in the AHC processing and were cartographically represented as "anomalous points."

The AHC classification principle is to sort each individual starting from as many classes as there are individual samples (i.e. 15,436 classes), and then progressively grouping the most similar samples together step by step into classes forming a hierarchical tree, until all individuals are regrouped into one single class. This hierarchical tree, called dendrogram, can be used to help the geologist understand the geochemical relationships between each clusters of the dendogram and choose the number of classes which are the most conservative to interpret on the clustered map (Martelet et al., 2006).

The AHC results crosschecked with field constraints support the individualization of 31 distinctive classes which can be interpreted in

terms of lithogeochemical groups based on their geochemical signatures presented in Table 4 (see their average elementary contents in Table A-3 in the Supplementary Material). Each single class represent the dominant signature of the small-scale combination of rocks outcropping in the ~10 km² sampled catchment area. Most of the geochemical signatures of the 31 lithogeochemical classes, whether expressed on the positive or negative pole, can be interpreted according to their elementary assemblages. Signatures combining the elements constitutive of minerals with typical granitic affinities are generally carried by Ca, Na, K, Ba, Sr, Mg (e.g. classes 1, 2 and 3 of Table 4). Variations in the average elementary contents in each class help to propose more precise interpretations in order to discriminate the tonalitic, granodioritic or granitic affinity of igneous intrusions at smaller-scale. Rare metals (Bi, Be, Cs, Li, Sn, W), whose contents are linked to the source signature and/or increase with magmatic differentiation, are classically associated in the positive signatures of classes 4, 7, 14, 15 and 24 (Table 4). These classes are respectively interpreted as alkali basalts, alkali granites, monzonites, peraluminous granites and differentiated leucogranites. Conversely, basic and ultrabasic signatures are represented by associations of Cr, Ni, V, Co, Cu, Fe on the positive pole such as in classes 5 (mafic basalts), 16 (ultrabasic rocks) and 22 (basic orthogneiss). Classes 23, 25, 26 and 29 represent metasedimentary associations consisting of schists and quartzite. These interpretations are mainly based on the negative poles signatures of these classes regarding to the group of elements with a magmatic (e.g. Rb, Sr, Th, Ba, Be, K, Na, Mg). However, some signatures are not trivial to decipher from a lithological proposal. This is for example the case of classes 8, 20, 21 or 26 which do not show without a clear positive or negative signature, and for which lithogeochemical interpretations are mainly based on the contribution of geological knowledge as well as the interpretations of the classes nearby. For example, class 8 presents a strong similarity and proximity in the AHC dendrogram with classes 9 and 10, which have positive signatures in Mn, Co, Fe and P, interpreted as strongly lateritic weathered environments. Field observations, geological descriptions and interpretations of the AHC results provide additional information that allows this class to be assessed as representing an undifferentiated lateritic signature. The cartographic distribution of the 31 lithogeochemical classes generated by the AHC is presented in Fig. 4. This cartographic plot of AHC classes displays strong relationships with geological and structural independent information, as well as colorimetric RGB maps produced from the PCA principal components (Figs. 1 and 3). This synthetic mode of representation of the stream sediment geochemical dataset provides a globally consistent cartographic view of some of the main geological domains identified during the geological survey, and is therefore very helpful for geological mapping programs. We also demonstrate that on a regional scale the effect of presence of several lithologies within the same watershed is not detrimental, contrary to the work of Najafian et al. (2023) which demonstrates that at the local scale of the watershed the dilution effect is clearly identified.

### b. U–K–Th pseudo-radiometric geochemical signal vs. airborne gamma ray geophysical data

The geophysical method of gamma ray spectrometry (so-called radiometry) is based on the detection of ground gamma-ray radiation using a spectrometer. This method detects both non-natural and natural radionuclides. Among the natural radionuclides contained in crustal rocks, only U, K, and Th produce gamma rays of sufficient intensity to be measured by radiometry (Minty, 1997). Each radioactive isotope, according to its decay chain, has a particular spectrum of gamma ray emission which allow, after ad hoc processing, the derivation of U, K and Th concentrations at ground level, within the first tens of centimetres of the surface. The radiometric signal is shielded by 30 cm of water and impacted to a lesser extent by surface moisture, dense vegetation, as well as topographic effects (Hovgaard and Grasty, 1997). Natural radiometric contents vary depending on the outcropping nature of the rock, i.
<caption>**Table 4**</caption>

Positive and negative geochemical signatures and lithological interpretations for each of the 31 classes derived from the AHC. Elements in brackets represent a lesser but still significant contribution to the geochemical signature. Nb. SMP = Number of sample.

<table><thead><tr><th rowspan="2">AHC classes</th><th rowspan="2">Nb. SMP</th><th colspan="2">Geochemical signatures</th><th rowspan="2">Interpretation</th></tr><tr><th>Positive</th><th>Negative</th></tr></thead><tbody><tr><td>1</td><td>877</td><td>(Ba, Ca, Mg, Sr)</td><td></td><td>Granite</td></tr><tr><td>2</td><td>523</td><td>(Ba, Ca, Mg, Na, Sr)</td><td>Bi, Cs, Hf, Mo, Nb, Sn, Ta, U, W</td><td>Granodiorite</td></tr><tr><td>3</td><td>455</td><td>Ca, Mg, Na, Sr (Ba)</td><td>Bi, Hf, Mo, Nb, Pb, Sn, Ta, Th, U, W</td><td>Tonalite (rich in plagioclase and amphibole)</td></tr><tr><td>4</td><td>224</td><td>(Fe, Mo, Nb, Ta, Ti, V, Au)</td><td></td><td>Alkali basalt (weathered basalt)/Alkaline syenitoide</td></tr><tr><td>5</td><td>172</td><td>Al, Cr, Mo, Nb, Ni, Sc, V (Cu, Fe, Ta, Ti)</td><td>Ba, K, Na, Pb, Rb, Tl</td><td>Basalt</td></tr><tr><td>6</td><td>134</td><td>Ti (Bi, Nb, Sn, Ta)</td><td>Al, Ba</td><td>Akonolinga rutile-rich schists</td></tr><tr><td>7</td><td>99</td><td>Mo, Nb, Ta, Zn (Ti, Y)</td><td></td><td>Alkaline granites with rare metals (sometimes acidic volcanism)</td></tr><tr><td>8</td><td>428</td><td>(Mn)</td><td></td><td>Laterite</td></tr><tr><td>9</td><td>126</td><td>Mn (Co, Fe, P)</td><td></td><td>Laterite on mafic rocks</td></tr><tr><td>10</td><td>33</td><td>Co, Fe, Mn, P</td><td>Hf, Li, Pb, Sn, Ti, W</td><td>Laterite on ultramafic rocks enriched in Mn/Co</td></tr><tr><td>11</td><td>989</td><td>(La, Ce)</td><td></td><td>Undifferenciated migmatite</td></tr><tr><td>12</td><td>373</td><td>(Cu, La, Mn, Ce)</td><td>Sb</td><td>Paraderived migmatite</td></tr><tr><td>13</td><td>130</td><td>Ge, La (Hf, Th, U)</td><td>Sb</td><td>Anatectic granite</td></tr><tr><td>14</td><td>183</td><td>Pb, U (Be, Hf, K, La, Rb, Sn, Th, Tl)</td><td></td><td>Monzonite</td></tr><tr><td>15</td><td>30</td><td>Be, Ge, Hf, K, La, Rb, Sn, Th, Tl, U, Y, Zn (Ca, Li, Mo, Na, Nb)</td><td>Co, Cr, Cu, Ni, P, Ti, V</td><td>Peraluminous granite with rare metals</td></tr><tr><td>16</td><td>39</td><td>Cr, Cu, Ni, Sc (Al, Ge, Mg, V)</td><td></td><td>Ultramafic rocks</td></tr><tr><td>17</td><td>397</td><td>Pb (Al, Ba, Cr, K, Sr)</td><td></td><td>Syenite</td></tr><tr><td>18</td><td>1084</td><td>(Al, Rb)</td><td></td><td>Aluminous leucocrate granite</td></tr><tr><td>19</td><td>742</td><td>Al (Mo)</td><td>Ca, Mn, Na</td><td>Bauxite (?)</td></tr><tr><td>20</td><td>1148</td><td></td><td>Fe, Mn</td><td>Porphyry granite rich in Si</td></tr><tr><td>21</td><td>1715</td><td></td><td></td><td>TTG Orthogneiss</td></tr><tr><td>22</td><td>594</td><td>(Cr, Cu, V)</td><td></td><td>Basic to tonalitic orthogneiss</td></tr><tr><td>23</td><td>285</td><td>(As, Bi, W)</td><td>Ba, Ca</td><td>Schist</td></tr><tr><td>24</td><td>169</td><td>Bi, Cs, Li (Be, Sn, U, W)</td><td></td><td>Differentiated leucogranite</td></tr><tr><td>25</td><td>23</td><td>As (Sb, W, Au)</td><td>La, P, Sr, Th</td><td>Lom schist</td></tr><tr><td>26</td><td>860</td><td></td><td>Sr</td><td>Volcano-sedimentary schist</td></tr><tr><td>27</td><td>1262</td><td>(Ba)</td><td>Cu, Fe, Li, Ni, Sc</td><td>Orthogneiss/Porphyritic granite</td></tr><tr><td>28</td><td>537</td><td>Ba, K (Rb, Sr)</td><td>As, Cu, Fe, Ni, Sc, Ti</td><td>Potassic granite</td></tr><tr><td>29</td><td>1237</td><td></td><td>Al, Ba, Be, Fe, K, Mg, Na, Rb, Sr, Tl</td><td>Micaschiste and Quartzite (locally Cretaceous sediments and lakeside sediments)</td></tr><tr><td>30</td><td>557</td><td></td><td>Al, As, Be, Co, Cr, Cu, Fe, Ge, La, Li, Mg, Mn, Mo, Ni, Rb, Sc, Th, Tl, U, V, Y</td><td>Tonalitic migmatitic orthogneiss</td></tr><tr><td>31</td><td>11</td><td>As, Sb, W (Cs, Au)</td><td>Ba, Ca, La, Na, Nb, Sr, Th</td><td>As-Sb-Au veins</td></tr></tbody></table>

e. its chemical content, often interpreted in terms of lithological affinity and degree of weathering (Cook et al., 1996). Airborne radiometric data are also more specifically used in the mining domain to quickly target specific lithologies known to be enriched in radionuclides (syenite, carbonatite, weathering area, U-bearing calcrete, etc.).

The cartographic representation of radiometric surveys is usually achieve through mono-elementary maps (U, K or Th), in order to highlight the ground concentration of each element, or as ternary maps, based on the RGB fusion of the three element contents. By convention, K, Th and U are displayed as red, green and blue respectively. As part of the PRECASEM project, an airborne geophysical survey (in magnetism and radiometry) was conducted across the whole project area, at an altitude of 150 m from ground level and with flight line spacing of 500 m. The radiometric maps are contrasted and seem to be locally significantly influenced by very different ground conditions varying from swamp and rainforest, to strong relief or arid areas, making *in fine* the interpretation of the gamma-ray signal complex.

On the contrary, stream sediments geochemical results shouldn't be affected by these external conditions. In this regard, a comparison of the radiometric U, K and Th data was performed with geochemical contents in U, K and Th obtained from stream sediments and later designed as "pseudo-radiometric" data (respectively Figs. 5-7). The cartographic representation of each element was produced by an IDW interpolation in order to have a continuous and homogeneous representation while preserving the true attribute values at the data location (Lima et al., 2005). The combination of U, K and Th interpolations provides a ternary representation of the geochemical pseudo-radiometrics in the RGB colorimetric space easily comparable with airborne radiometrics U, K and Th plotted in the same color space (Fig. 8).

Concentrations of these three elements in our analysed stream sediments are in the ranges 0.5-47.8 ppm for U, 0.06-5.46 % for K and 2.4-251.0 ppm for Th (Table 2), systematically encompassing the average abundance value of those elements in the continental crust (2.7 ppm for U, 2.09% for K and 9.6 ppm for Th after Taylor, 1964). The high U (>8 ppm) and Th (>35 ppm) values mainly indicate intrusive alkaline and hyperalkaline fractionated massifs of syenite, monzonite and granitoids attributed to the late-orogenic Panafrican magmatic suites. U and Th also delineates some of the main bodies of anatectic magmas produced along the high-temperature Panafrican shear zones, especially in the Tcholliré-Banyo and Nyong-Bayomen areas. Strongly U and Th depleted terranes closely matches with old pre-Panafrican migmatitic Archean, Paleoproterozoic or Tonian basement of all the main geological domains of Cameroon (i.e. Nyong-Bayomen, Bafia, Ntal, Bindiba-Rinda, Bamenda-Tignère). The high potassium contents, above 1.8 %, represent the large magmatic batholiths of the calco-alkaline Bamenda-Tignère and Extended Congo Craton Domains of Central Cameroon on the Linté, Yoko, Deng Deng, Ngaoundéré, Tignère and Tchamba sheets (Fig. 1; Monod et al., 2019; Mero et al., 2019; Couëffé et al., 2019; Bernard et al., 2019a; Blein et al., 2019; Ngako et al., 2019). Some orthogneissic domains attributed to the Tonian to late-Tonian Suites on the Bafia sheet are also strongly enriched in potassium (Monod et al., 2019). Low K-content seems to be related to the large para-derived Yaoundé Sub-Domain and, to a lesser extent, to the pre-Panafrican migmatitic basement of Cameroon (Fig. 1). Lateritic and bauxite plateaus and tertiary volcanic flows also appear strongly depleted in K.

Comparisons between airborne radiometry and geochemical U, K, Th contents show: 1) outstanding analogies between the two types of data; and 2) first order cartographic geometries consistent with the main regional-scale geological and structural data (Fig. 9).

However, even if the airborne survey has a better resolution compared to the density of 1 sample/≈ 10 km² applied to the stream sediment survey, the interpolated geochemical ternary U, K and Th combination highlights some additional geological information. The most illustrative example is the E-W contact zone around 5°N between the calc-alkaline granitic series of the Adamawa-Yadé domain and the
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Cartographic representation of the lithogeochemical interpretation results together with the main regional structures and the main geological boundaries extracted from the new geological map of Cameroon (Delor et al., 2021) on a topographic background.</figcaption></figure>

metasedimentary series of the Yaoundé domain (Fig. 9). This major structural contact zone (NBSZ, Fig. 9) is well illustrated in the geochemical pseudo-radiometric map while almost invisible in the airborne radiometry (Fig. 8). On the contrary, the structural TBSZ and CCSZ features, separating the Bamenda-Tignere and the extended Congo Craton domains (Fig. 1), are much more visible on the airborne radiometry (Fig. 7). The thick forest cover and the humid and swampy environment of this area, which generate significant perturbations in the airborne radiometric data, may explain this difference. On the same note, the Adamawa-Yadé batholith in the middle of the studied area appears to be much more contrasted and with a higher K signal on the geochemical pseudo-radiometric map than on the airborne geophysics, which is blurred by a pale-blueish ubiquitous shade (Fig. 9). This can be explained by the thick lateritic profile and duricrust developed in this region at the expense of the Adamawa plateau, which may alter and/or prevent radiometric airborne acquisition to receive any pristine high-K signal from the granitoïds covered by a deep K-depleted weathering profile. Stream sediments data appears on the contrary to have a lesser sensibility to the presence of such strong lateritic weathering profile and a better ability to provide information on the bedrock.

# 5. Discussion

## a. Efficiency of the lithogeochemical map

The methodology presented in this study combines the results of country-scale stream sediments geochemical data processed both by PCA and AHC. The cartographic distribution of the 31 lithogeochemical classes generated by the AHC (Fig. 4), even without any field-crossed analyses, immediately reveals that the interpreted classes are not randomly distributed across the studied area. They point to different and quite well delineated lithological domains sharing uniform and consistent signatures wherever they are located along central Cameroon, in coherence with the conclusions of similar works performed in different geological contexts and at smaller scale (Ferreira et al., 2001; Kirkwood et al., 2016; Vicente et al., 2021; Eskdale et al., 2023).

Once compared to the geological maps, each lithogeochemical signature can be associated to one of the main recognized lithostructural Domains (or Sub-domains) of Cameroon which are underlined by sets of very distinct AHC classes (Figs. 1 and 4).
<figure><img src="image_6.png" /><figcaption>Fig. 5. Comparison for uranium of the airborne radiometric signal (left) and the IDW interpolation of its geochemical content (right).</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. Comparison for K of the airborne radiometric signal (left) and the IDW interpolation of its geochemical content (right).</figcaption></figure>

- Classes 6, 26 and 29, are characteristic of the Yaoundé Sub-domain of the Extended Congo Craton. These classes are characterized by negative geochemical signature in Al, Ba and Sr showing a remarkable cartographic coherence with the biotite- and chlorite-bearing schists and micaschists formations of the Yaoundé Group. In agreement with geological field observations (Delor et al., 2021), this signal is delayed toward the West as the metamorphic grade

increases until gradually moving to a para-derived migmatitic signal carried by the AHC classes 11 and 12. These migmatitic rocks extend further West in the Nyong-Bayomen Domain, which is actually known for its high-grade anatectic gneisses (Toteu et al., 1994, 2001; Lerouge et al., 2006);

- Classes 7, 18, 20, 24, 27 and 28 define the voluminous batholiths of Panafrican age constituting the basement of the Adamawa-Yadé Sub-
<figure><img src="image_8.png" /><figcaption>Fig. 7. Comparison for Th of the airborne radiometric signal (left) and the IDW interpolation of its geochemical content (right).</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. Comparison of geochemical signal in stream sediments and airborne radiometry. Left: ternary airborne radiometric signal; Right: ternary representation in RGB colorimetric space of IDW interpolations of geochemical contents in U, K and Th.</figcaption></figure>

domain as well as some plutons emplaced in the northern part of the Bamenda-Tignère Domain. Our regional-scale lithogeochemistry data allows dividing the large Adamawa-Yade batholith into several individual batches of plutons characterized by distinctive geochemical signatures. These geochemical grouping may be of first importance to assign individual plutons to a given magmatic Suite, which is sometimes tricky to do on the field due to local outcrop-scale facies

variations and require extensive whole-rock geochemical analyses. Regional-scale stream sediment lithogeochemistry can thus be used as guideline in magmatic environments to drive whole-rock geochemical sampling campaigns;

- Classes 1, 2 and 3 are mainly found in the northern part of the Bamenda-Tignère domain, characterized by the presence of voluminous Panafrican plutons and batholiths emplaced into a juvenile
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Map of the ternary representation in the RGB colorimetric space of the IDW interpolations of the geochemical contents in U, K and Th with the superposition of the main regional structures and the geological contours extracted from the new geological map at 1: 500,000-scale modified from Delor et al. (2021).</figcaption></figure>

Paleoproterozoic basement (Delor et al., 2021). If there is little petrological difference on the field between the Panafrican granitoids emplaced in the Bamenda-Tignère and Adamawa-Yade domains, the lithgeochemical signature show that the ones emplace in Bamenda-Tignère are more calci-sodic than those of the Adamawa-Yade. This difference may reflect a large-scale discrepancy in the source of the magmatism, generated through a juvenile Paleoproterozoic block in Bamenda-Tignère Domain and into a reworked and decarbonized Archean block in the Adamawa-Yade. Country-scale lithgeochemistry seems thus to be able to give first order information related to crustal-scale geochemical reservoirs involved into melting and differentiation processes;

- Classes 21, 22 and 30 match very well with most of the old-looking highly deformed migmatitic gneisses of the Nyong-Bayomen Domain, in the South of the Bamenda-Tignère Domain, and above the Ntal and Bindiba-Rinda complexes as well as around Linté in the Adamawa-Yade Sub-domain. Geochronological U-Pb dating of rocks collected in these places demonstrate that they all belong to pre-Panafrican geological units, of Archean (Ntal, Bindiba-Rinda and Nyong-Bayomen), Paleoproterozoic (Nyong-Bayomen and Bamenda-Tignère) or late Tonian (Linté) ages (Delor et al., 2021). Ntal and

Bindiba-Rinda Archean complexes, as well as the juvenile Bamenda-Tignère Paleoproterozoic basement highlighted by the cluster of points belonging to classes 21, 22 and 30, were not known on historical geological maps;

- Classes 4, 5 and 7 are restricted to the volcanic plateaus of the Cameroon Line in the vicinity of Ngaoundéré (northeast), Tignère (northwest) and Bafoussam (southwest) main cities. Lithogeochemistry data point to the presence of two main endmember: an alkaline group encompassing both basalt and more differentiated rocks (phonolite, trachyte up to rhyolite and alkali-granitoïds) and a more mafic group mainly composed of basalt. Lithogeochemistry may thus be used at broad scale to discriminate specific series in the volcanic pile and help understand the timing of emplacement and/or source of the volcanic sequences. The alkaline affinity of the Cameroon volcano-plutonic Line (Fitton and Dunlop, 1985; Njonfang et al., 2011; Njome and de Wit, 2014) appears to be documented by lithogeochemistry regardless of the degree of differentiation of the rocks, since AHC classes 4 (alkali basalt), 5 (basalt) and 7 (alkaline granitoïds) are statistically close from each other despite counterintuitive signature. On the same note, the presence of the Akonolinga rutile-rich schists of AHC class 6 is statistically close from classes 4, 5
and 6, while geologically very distinct, may be explained by the Ti-, Nb- and Ta-rich signature of those very specific rutile-bearing schists (Maurizot et al. 1986; Loislard and Herrouin, 1992; Stendal et al. 2006; Knudsen et al. 2013), making it lithogeochemically close from the alkaline or rare metal rich signature of the Cameroon Line products. External geological constraints are here of primary importance to perform robust interpretations. Our data are finally not able to fully differentiate alkaline volcanics of the Cameroon Line from the alkaline Tonian plutonic Suite of Ntui (Bafia sheet). Both signatures are statistically equivalent and identification of the two would need a reduction of the scale and/or a focused statistical approaches;

- Class 17 is strongly correlated with late-Panafrican ovoid plutons of syenitic composition, mainly located in the Linté sector and poorly known before the present project. The Bi- and Li-rich signature of the class 24 point to differentiated leucogranitoïds. Class 25 is restricted to the volcano-sedimentary schists of the Lom basin (Vairon et al., 1985; Soba et al., 1991; Toteu et al., 2006b). Classes 16 and 10 point respectively to ultra-basic bodies of the Yaoundé basin and to the associated Mn-Co lateritic profiles. All these classes associated to specific geological bodies highlight the ability of regional-scale lithogeochemical survey to isolate local "exotic" lithologies, somewhat less critical for regional-scale geological mapping but which could be of first importance for mineral resources purposes (see below).

Within each domain, the cartographic distribution presented on Fig. 4 is more subtle and lithogeochemistry data points out geological targets that should be checked in the field, as priorities, during mapping surveys.

Whilst several classes have signatures that can be easily interpreted in terms of lithology, the significance of several others is trickier. Signatures dominated by Cr-Mg-Ni-(Cu-Sc) can be attributed to ultramafic or mafic rocks. The Mo-Nb-Zn-(Ti-Y) signature is representative of rare metal-bearing alkaline granites whereas the Mn signature is related to highly lateritized areas. However, some lithogeochemical signatures defined by the AHC are difficult or not interpretable at all. For example, it is difficult to distinguish in-the-field differences within the meta-sedimentary affinity classes of the Extended Congo Craton (e.g. classes 23, 26 and 29). Targeted field checks and/or the use of existing geological maps are necessary to understand the meaning of such signatures. In addition, the choice of the number of PCA components and the number of AHC classes is not always trivial and requires a kind of prerequisite knowledge of local geology. This choice is not arbitrary and is an expert-driven process. With a sampling density of one sample per 10 km², it is clear that geochemistry on stream sediment cannot, on its own, contribute to precisely draw the lithological contacts of a geological map, although, within the framework of this project, the sampling density is particularly suitable to support a 1:200,000-scale mapping. At this specific scale, the regular spatial distribution and homogeneous density of the sampled points across the entire study area make the dataset suitable to classical statistical processing used here, and does not require more complex approaches (e.g. Yousefi et al., 2013; Lancianese and Dinelli, 2015; Parsa et al., 2017).

Despite some well-known inherent flaws of stream sediment-derived lithogeochemistry, such as mixing of rock types in some of the sampled catchment, possible anthropogenic contamination or soil and/or weathering dilution of the signal (Eskdale et al., 2023), our data demonstrate that a country-scale survey driven by best-practice sampling procedures and standardized data processing is able to capture most, if not all, of the major regional-scale geological features of the studied area and can be used as an input data for geological mapping programs, at the same level than airborne geophysics, while cost-efficiency, timing and duration of both surveys should be carefully evaluated.

This method is also particularly efficient for a predictive approach to

geological formations in areas that are poor in rock outcrops (with thick lateritic cover, wind deposits, sand, etc.) or hardly accessible, and allows to capture the first order geological background and main lithological contrasts of such remote sectors (e.g. in the national parks forests of Yoko and Deng Deng).

Information regarding a certain type of mineral resources can also be highlighted. For example, the Al-rich lateritic signatures carried by class 19 in the known bauxite plateaus of Minim Martap (NE of the Tibati sheet) and Ngaoundal (Bagodo sheet) (Belinga, 1972; Hiéronymus, 1973; Momo et al., 2012, 2019; Tematio et al., 2015; Sojien et al., 2018; Dongmo et al., 2019), are highlighted in other sectors such as the south-western part of the Tibati sheet and southern part of the Ngaoundéré sheet, which may have bauxite potential. The northern part of the well-known alluvial and eluvial rutile district of Akonolinga is shown by class 6. Rutile, that mainly derives from metapelitic units of the Neoproterozoic Yaoundé Group and was formed during the Panafric anogeny (Yarmoluk, 1980; Vairon et al., 1985; Milési et al., 2006; Stendal et al. 2006; Knudsen et al. 2013), has been the subject of exploration and exploitation works (Maurizot et al. 1986; Loislard and Herrouin, 1992). The alkaline and hyperalcaline intrusions of syenite mapped by class 17 may have, in some circumstances, a potential in Nb, Ta, REE, and U. Similarly, ultramafic complexes of gabbros, pyroxenite and amphibolo-pyroxenite, recognized by geological fieldwork, are well mapped by class 16. These complexes can be highly prospective for primary Cu, Ni, and PGE mineralization, but also for lateritic enrichments in Ni-Co-Mn-(Cr) as those identified in the Lomé complex of south-eastern Cameroon (Seme Moungue, 1998; Toteu et al. 2006b; Geovic Mining Corp, 2011; Lambiv Dzemua and Gleeson, 2012). Rare metals, necessary for energy transition technologies, are well represented in classes 7 and 15 which sign, among other things, granites of the district exploited for tin and subordinated rare metals (W, Nb, Ta) at Mayo Darlé (Nguere, 1982; Thierry and Loislard, 1986; Kinnaird et al., 2016; Tantoh et al., 2023; Lavenir et al., 2023) and the younger granites types peraluminous intrusions that form the border with Nigeria near Tchamba (Wright, 1985). Furthermore, some of the anomalous points that have been removed from the statistics in order to avoid skewing of the dataset confirm the metallic anomalies linked to these classes for Co, Ni, Cr and Mn. Finally, the contacts between strongly contrasted lithologies, as represented on Fig. 4, must also be the focus of field survey as they may represent major structures potentially hosting ore deposits, especially gold.

## b Radiometric mapping

Airborne geophysical surveys are nowadays very commonly performed as part of geological mapping programs and used to spatialize punctual geological observations. However, not all mapping projects benefit from such data, which can be expensive depending on the covered area and/or specifications. The tests carried out in this study have shown that a suitable processing of the analytical results of the U, K and Th elements measured into stream sediment samples can be successfully used to reconstruct a set of pseudo-radiometric maps, equivalent to a certain extent, to an airborne radiometric geophysical survey. The accuracy of these two types of U, K, Th maps is strongly dependent of the resolution of the airborne geophysical survey in one hand, and of the density of the geochemical samples in the other hand. Our data shows that country-scale stream sediment survey at a density of 1 per 10 km² compares very well in the identification of the main geological domains to the airborne radiometric survey, and drastically extend similar conclusions obtained at ground level and smaller scale (Lima et al., 2005). The processing of geochemistry data may be a powerful substitute for airborne radiometry, allowing a quick identification of radiometric mineral occurrences (e.g., uraniferous calcrete, carbonatite, syenite), as well as large-scale geological units and/or targets to be checked in the field. Moreover, the lithogeochemistry is able to bring an important set of information and element-associations with around 50
analysed elements, while the radiometry only provides a signal from three of them. Future developments would probably aim at deciphering geochemical relationships between U, K, Th and the other chemical elements in stream-sediments dataset in order to develop local proxy able to be spatialized on a prospective territory through airborne geophysical surveys.

Ground geochemistry can also be particularly useful in wetlands and/or areas with dense vegetation cover, which can generate significant perturbation in airborne radiometry. In tropical environments, such as the central and southern parts of Cameroon, the density of vegetation as well as, locally, swampy areas, significantly affect the quality of the airborne radiometric signal but has little or no impact on the quality of the geochemistry results on stream sediments. In addition, steep relief may disturb airborne radiometry when flight ground clearance exceeds normal correction possibilities, but will have almost no impact on geochemistry data. The best example of that lies in the south-western corner of the studied area across the Ndikiniméki 1:200,000 sheet (Makitie et al., 2019): airborne radiometrics display a uniform purple hue (Fig. 8) interpreted as a slope-linked artefact whereas in contrasts, U–K–Th stream sediment signature is much more detailed and delineates SW-NE oriented bodies of Th-enriched rocks compatible with the slices of high-grade gneisses pinched between sub-vertical shear zones as described by geological investigations (Makitie et al., 2019; Delor et al., 2021). On the same note, the lateritic plateaus, which can reach several meters in thickness in the Adamawa-Yadé Domain (Bagodo, Tibati and Ngaoundéré sheets), also strongly influence the airborne radiometric signal (Figs. 8 and 9). Ternary radiometric shows a relative enrichment of U and Th content together with K depletion, despite the granitic substratum of the plateaus and much probably linked to subsurface weathering-related geochemical processes, making it difficult to decipher underlying geological units. In comparison, the reconstruction of the ternary signal by combining the stream sediment geochemical results on U, K and Th highlights well-marked contrasts between undercover geological formations. All these contrasts must be checked in the field as they may be the signature of structures hosting mineral deposits (e.g. gold bearing shear zones).

## 6. Conclusion

Modern geological mapping programs at the scale of 1: 100,000 or 1: 200,000 conducted at the regional or country scale with increasingly limited time-to-completion objectives must be able to combine several methods (field mapping observation, geophysical interpretation, remote sensing data interpretation, geochemical survey, etc.) in order to produce updated and upgraded high-quality maps in a strict timetable.

One of the main objectives of the method and dataset presented in this paper is to provide basic and/or additional information on large-scale areas with poor or no geological knowledge (i.e green field areas). These methods, derived from the various multivariate statistical approaches, based on geochemical interpretation of a country-scale stream sediment surveys, can be reproduced regardless of the scale of the map and/or the sampling density of stream sediment survey.

The approach of multivariate statistical analysis by coupling a PCA and an AHC on an exceptionally rich and homogeneous dataset of 15,463 samples (48 elements analysed, plus gold), aims to demonstrate the importance of performing a strategic geochemistry survey on stream sediments ahead of a geological mapping campaign, and the benefit to include it into any ambitious geological mapping programs in green field areas. Lithogeochemical interpretative maps enable the representation of a first predictive sketch map of the main lithological groups cropping out in the study area. It is a powerful tool to define field targets, saving a significant amount of time for field investigations and reducing drastically the probability to miss resource-bearing lithologies during geological field surveys. However, one of the limits of the method lies in the difficulty of interpreting complex signatures of geochemical mixtures, which need to be complemented by field observations.

The reconstruction of the pseudo-radiometric signal by processing U, K and Th analytical results and its positive comparison to geophysical airborne radiometrics demonstrate the ability of stream sediment geochemical surveys to be powerful alternative or complementary cartographic solution to airborne radiometric surveys in geological mapping and resources programs. The accuracy of these two types of radiometric maps and therefore their complementarity depend on the resolution of the airborne geophysical survey in one hand, and the density of the geochemical samples on the other hand. When no airborne radiometric data is available, the processing of geochemistry data is a good substitute, allowing quick identification of the main large-scale geological domains, major lithospheric structures and exotic potential resource-bearing lithologies. Even for a relatively low-density country-scale geochemical sampling plan, it is shown that outstanding information is provided by the geochemical U, K, Th maps in areas where the geophysical signal is perturbed for various reasons. This work therefore concludes on the importance of considering the statistical processing of local-to regional-scale stream sediment geochemical datasets as an outstanding method for the ongoing programs of geological mapping worldwide.

## CRediT authorship contribution statement

G. Vic: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. J. Bernard: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. M. Chevillard: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. L. Bailly: Writing – review & editing, Writing – original draft, Validation, Methodology, Data curation, Conceptualization. C. Ndongue: Resources, Investigation, Data curation. Y. Itard: Writing – review & editing, Validation, Methodology. J. Melleton: Writing – review & editing, Supervision, Methodology. G. Martelet: Writing – review & editing, Validation, Supervision, Software, Methodology, Data curation. E. Kouokam: Project administration, Funding acquisition. E. Fournier: Writing – review & editing, Validation, Software, Methodology. R. Couëffé: Writing – review & editing, Writing – original draft, Validation, Data curation. D. Keutia: Writing – review & editing, Data curation.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgement

The authors thank all Consortium's contributors for their help in getting this paper achieved, with special acknowledgment to Dr. G.S. Mananga, head of the PRECASEM program for his support, especially during the first phase (2016–2018) of the project during which this study was realised. We also thank the Ministry of Mine, Industry and Technological Development (MINMIDT), to which all data belong, for encouragements in publishing these scientific results. We thank C. Spencer and R.D. Tucker for their fruitful discussions about an early version of this manuscript.

## Appendix A. Supplementary data

Supplementary data related to this article can be found at https://doi.org/10.1016/j.apgeochem.2025.106321.
# Data availability

The authors do not have permission to share data.

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