Identifying and quantifying source contributions of aeolian sediment is critical to mitigate local and regional effects of wind erosion in the arid and semi-arid regions of the world. The purpose of this study is to apply sediment source fingerprinting methods to determine the source contributions of the aeolian sands of a small erg with varied and complex potential sources upwind. A two-stage statistical processes was applied to select optimum composite fingerprints to discriminate the potential sources of the aeolian sands from the Jazmurian plain located in southern Kerman Province, southeastern Iran. A modified Bayesian un-mixing model was applied to quantify uncertainties associated with the source contributions, and the model was evaluated by a mean absolute fit (MAF) method. The results suggest that four geochemical properties (Cr, Co, Ni, and Li) were the optimum fingerprints for solving the modified Bayesian un-mixing model. The results show that there is great diversity in terms of the sources of sand, and that, contrary to expectation, sediments associated with an adjacent large ephemeral lake are the least significant in supplying sediment to the erg. Sand-sheet-derived sands and alluvial sediments dominate the majority of samples, and are likely attributable to relatively short-distance aeolian flux, but substantial contributions from alluvial fans and terraces likely represent longer distance pathways. These results highlight the need to consider sediment provenance on a site-by-site basis. The MAF evaluation showed that the modified Bayesian un-mixing model is an effective method to aid aeolian sediment fingerprinting. This method may be applied to assess aeolian sediment sources in other desert regions with strong aeolian activities.

识别和量化风沙沉积物的来源贡献对于减轻世界干旱和半干旱地区风蚀的局部和区域影响至关重要。这项研究的目的是应用沉积物源指纹法来确定小erg的风沙的源贡献，这些小erg的风向具有多种多样且复杂的潜在源。应用了两个阶段的统计过程来选择最佳的合成指纹，以区分来自位于伊朗东南部克尔曼省南部的贾兹穆里平原的风沙的潜在来源。应用改进的贝叶斯混合模型对与源贡献相关的不确定性进行量化，并通过平均绝对拟合（MAF）方法对模型进行评估。结果表明，四种地球化学特性（Cr，Co，Ni，和Li）是求解改进的贝叶斯混合模型的最佳指纹。结果表明，在沙源方面存在很大的差异，与预期相反，与相邻的大型临时湖相关的沉积物在为erg提供沉积物方面的重要性最低。大部分样本都来自于砂层的砂土和冲积物，并且可能归因于相对短距离的风成因，但是冲积扇和阶地的大量贡献可能代表了较长的路径。这些结果表明，有必要逐点考虑沉积物来源。MAF评估表明，改进的贝叶斯非混合模型是辅助风沙沉积物指纹识别的有效方法。