The univariate statistics of potassium (K) and thorium (Th) concentrations in the oceanic and continental crust of the Earth has been recently investigated from geochemical databases and airborne radiometric surveys [1]. This study demonstrates that the frequency distributions of these elements are scale-dependent. There are right-skewed for small-scale samples (typical volume of rocks analyzed for an individual sample in a dataset) but tend to be more symmetric for large-scale samples. The right-skewed behavior of K and Th is attributed to their incompatible behavior during partial melting or fractional crystallization. The scale-dependence and evolution toward normal distributions are a direct consequence of the central limit theorem applied to K and Th concentrations. The results of the results of this study may be applied to Mars, using the Mars Odyssey global maps of K and Th concentrations [2]. In light of available K, Th concentrations at the rock-scale (in-situ samples and martian meteorites), we infer that each “pixel” of these maps reflect a right-skewed distribution of K and Th concentrations at smaller-scales, where K and Th-poor rocks, such as basalts, are spatially dominant. In turn, K, and Th-rich rocks, such as those found by Curiosity at Gale crater [3], may occur globally, though their spatial extension must be limited to account for the values reported by Mars Odyssey. The global, but sparse occurrence of K, Th-rich rocks at the surface is consistent with a buried felsic crust, outcropping at the favor of tectonic or impact events. These conclusions will be discussed in the context of the inferred constraints about the structure of the martian crust from Insight data [4]. [1] Baratoux, et al., Earth and Space Science, in press. [2] Boynton et al. JRGP, doi:10.1029/2007JE002887. [3] Sautter et al. doi:10.1038/NGEO2474. [4] Knapmeyer-Endrun et al., Science, doi: 10.1126/science.abf8966

Artisanal and small-scale gold mining (ASGM) represents a significant economic activity for communities in developing countries. In south-eastern Senegal, this activity has increased in recent years and has become the main source of income for the local population. However, it is also associated with negative environmental and social impacts. Considering the recent development of ASGM in Senegal, and the difficulties of the government in monitoring and regulating this activity, this article proposes a method for detecting and mapping ASGM sites in Senegal using Sentinel 2 data and the Google Earth Engine. Two artisanal mining site in Eastern Senegal are selected to develop and test this approach. Detection and mapping are achieved using Principle Component Analysis (PCA), Separability and Threshold (SEaTH) and Support Vector Machine classifier (SVM). The results are validated by ground-truth observations. The PCA indicates that the best period for identifying artisanal mining sites against other types of land-use is the end of dry season, when vegetation is minimal. This result is confirmed by examining the spectral evolution over time of different types of land-use. Input variables for SVM classification are defined by the SEaTH. The classification results are presented as a map with 5 color-coded categories of land-use. The method can be used to map the evolution of mining sites as a function of time using future Sentinel acquisitions. This approach may also be extrapolated to other areas in the Sahel where authorities are also confronted with the difficult regulation of artisanal gold mining activities in remote areas.