The Influence Of Surface Cover And Bedrock Geology On The Snow Geochemistry – An Example From Northern Finland

The idea of using snow in mineral exploration is due to the needs of environmentally friendly sampling methods for the ecologically sensitive northern areas. Not only the environmental issues, but the low costs of sampling and relieving permission issues encourage researchers to find new methods for mineral exploration. Surface geochemical methods, including sampling plants, topsoil horizons and snow can be considered in the areas where machinery is not allowed. Moreover, surface geochemical methods can provide the information of metal ions derived from the deep-seated mineralization below. The advantages of snow sampling are low volume of sample material, (comparably) light sample material and sampling equipment and therefore the option for low impact sampling campaign by skies or snowshoes.
In the New Exploration Technologies (NEXT) project*, 165 snow samples together with 13 field duplicate snow samples for quality control, were collected in March-April winter 2019. The aim was to estimate with statistical methods the usage of snow as a sampling material for mineral exploration. The samples were collected on the Rajapalot Au-Co prospect in northern Finland, 60 km west from Rovaniemi, operated by Mawson Oy. Stratified random sampling method was used to calculate sampling locations with balanced number of points per soil type and geophysical parameter, but randomly distributedwithin the strata over the test area. The samples were analysed in the Research Laboratory of the Geological Survey of Finland using a Nu AttoM single collector inductively coupled plasma mass spectrometry (SC-ICPMS) and returned analytical results for 52 elements at ppt level.
Of the analysed elements Ba, Ca, Li, Mg, Mo, Rb, Sr and V passed the strict quality control and were used for the final data analysis. Prior to statistical methods, the geochemical data was transformed to log-ratio scores in order to ensure that results are independent of the selection of elements and to avoid spurious correlations (compositional data approach). The results indicate strong dependency of the snow element composition to the soil type, meaning that there is systematic shift of element pattern if the snow sample was taken above mineral soil or organic soil. Thus, the soil type should be included in models to predict (geological) features below the surface or interpretation of snow data should be performed separately for different soil types. The impact of subsurface features on the snow geochemistry could only be tested indirectly by using geophysical data as proxy for characteristics of the basement rock. Based on linear models, it seems that snow geochemistry could be used as a mapping tool for delineating the areas of major geological units. Given the selection of analytical available elements, snow sampling could serve as a proxy where to continue exploration with different methods rather than directly pointing out the mineralized zones.