This research was financially supported by the National Natural Science Foundation of China (NNSFC, Project No. 42002298), the Chinese Geological Survey (Project Nos. DD20201181, DD20211403), the National Key Research and Development Program of China (NKRDPC, Project No. 2017YFC0601501) and funded by The Project of "Big Data Analysis and Major Project Evaluation of Strategic Mineral Resources" from the Chinese Geological Survey.
The identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral exploration. The various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical data. To map anomalies in a discrete field model of such data, two corrections are required: background correction and downstream dilution correction. Topography and geomorphology are important factors in variations of element content in stream sediments. However, few studies have considered, through the use of digital terrain analysis, the influence of geomorphic features in downstream dilution correction of stream sediment geochemical data. This study proposes and demonstrates an improvement to the traditional downstream dilution correction equation, based on the use of digital terrain analysis to map single-element anomalies in stream sediment geochemical landscapes. Moreover, this study compares the results of analyses using discrete and continuous field models of stream sediment geochemical data from the Xincang area, Tibet. The efficiency of the proposed methodology was validated against known mineral occurrences. The results indicate that catchment-based analysis outperforms interpolation-based analysis of stream sediment geochemical data for anomaly mapping. Meanwhile, the proposed modified downstream dilution correction equation proved more effective than the original equation. However, further testing of this modified downstream dilution correction is needed in other areas, in order to investigate its efficiency further.
XIANG Jie, XIA Peng, XIAO Keyan, Emmanuel John M. CARRANZA, CHEN Jianping.2023. Single-element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis[J]. Acta Geologica Sinica(),97(1):149-162Copy