Enhancing geochemical background estimation using rock weathering.

[Display omitted] • A stream sediment geochemical survey is conducted in a pilot area in central Iran. • Elastic velocities, porosity, and Schmidt hammer rebound values are measured. • An index for rock weathering rate in the case study is presented. • Introducing the weatherability explains a part of geochemical variability. • Regression model has some limitations highlighted by the results. The chemical composition of stream sediments is heavily influenced by weathering processes occurring in upstream lithological units. Despite their importance, these processes are frequently ignored in the examination of stream sediment geochemical data when identifying mineralized zones. Instead, existing geochemical background estimation procedures rely on the area size of upstream rocks as an indicator of their contribution to elemental concentrations of sediments. To address this limitation, a stream sediment geochemical survey was conducted in a pilot area in central Iran, and the physico-mechanical properties of upstream rock types, including elastic velocities, porosity, and Schmidt hammer rebound values, were evaluated. A new weathering index, "W ij ," was developed, utilizing proxies of rock weathering to estimate the geochemical background attributed to lithology at sediment sampling sites. The regression model was constructed by combining weathering proxies (W ij) with other independent variables, including the presence or absence of rocks in catchments, area size (X ij), and the weathering-corrected contributing factor (X ij W ij). The results suggested the significant capability of weathering proxies in explaining the geochemical variability of major elements such as Al, K, Fe, Ca, and Na. However, the regression model using weathering-corrected contributing factors delivered better overall accuracy than other independent variables. Nevertheless, our results also identified some limitations of the application of regression model in stream sediment data analysis. [ABSTRACT FROM AUTHOR]