## 3 Results and Discussion
### Discussion  
Regional geochemical soil datasets have been used to establish background values and generic reference values by lithological groups [16,17]. Sampling design often includes a random location of samples within the cell of a grid, or a supervised location in the cell following lithological criteria to obtain a representative sampling of all lithologies. Both approaches involve avoiding anthropogenically polluted areas, including cities, roads, dirt roads, waste dumps, etc. This is an important point when trying to find out the prospective significance of ore deposits to those data since the multi-element data are only able to reveal regional background anomalies with an expected low statistical significance. For this reason, a sufficient relationship must be established between the substrate and the soils developed on it, because many factors can compromise the correct interpretation of anomalies: compositional variations in bedrock, differential mobilities between groups of elements, as well as variations produced by pedogenesis processes. In the present work, we used a set of soil geochemistry data in two synclines with abundant mineral deposits (Hg in the Almaden syncline and Sb in the Guadalmez syncline) and determined through a cluster of variables that there was a significant relationship between the multi-elemental contents and the entirety of both synclines, and the four types of lithological units (ASQ, igneous rocks, quartzite-sandstones, and slates) present on each one [3,4,8,10]. The most commonly used statistical treatments for soil geochemical datasets are multivariate, principal component analysis (PCA), and factor analysis (FA). Both treatments are employed for different reasons: PCA is used to decompose the data into a smaller number of components, while FA is used to understand the underlying \"cause\" for which these factors (latent or constituents) capture much of the information of a set of variables in the dataset [34,35]. Therefore, while the aim of PCA is to explain the cumulative variance in as many variables as possible [36], FA focuses on explaining covariances or correlations between the variables. In this work, we followed the recommendations of [PERSON] et al. (2002) [37], removing outliers before a FA with varimax rotation. In order to select elements for each factor, a loading threshold of 0.5 was selected. The distribution maps of the groups of factor elements were created using the factor scores of each element, the aim of which was to obtain a normalized value for each sampling point. The IDW method was used to avoid detecting trends in the data by kriging that would lead to errors in the interpretation [38,39]. Figure 5: Spatial distribution maps (**A**) and violin plots (**B**,**C**) of trace elements (all data are expressed as mg kg\({}^{-1}\)). ASQ: alternations of shales and quartzites, IR: igneous rocks, QS: quartzites and sandstones, SI: slates. Element grouping showed five factors: Factor 1 (Al, Be, Co, Cr, Cu, Fe, Ni, P, Sc, V, and Zn), representing 45.1% of the variance; Factor 2 (Al, K, Mg, Hg, La, Ni, and P), representing 13.7% of the variance; Factor 3 (Ca, Ba, Co, Mn, and Sc), explaining 7.5% of the variance; Factor 4 (Pb and Zn), representing 6.4% of the variance; and Factor 5 (As and Sb), representing 4.6% of the variance. It was possible to distinguish several factors with a clear lithological control (F1 and F3), others with a clear control by mineralized phases (F4 and F5), and a mixed factor (F2), dependent on certain lithological units but also on the dispersion of Hg in the largest mining district in the world [40]. Before interpreting the distribution of these factors in the two synclines, one must bear in mind that this statistical treatment offers possibilities in terms of detecting the prospective zones for certain types of mineralization, but it also has serious limitations. [PERSON] et al.