Aeolian sediment fingerprinting using a Bayesian mixing model

Gholami Hamid; Telfer Matt W.; Blake William H.; Fathabadi Abolhassan

摘要

Abstract >Identifying sand provenance in depositional aeolian environments (e.g. dunefields) can elucidate sediment pathways and fluxes, and inform potential land management strategies where windblown sand and dust is a hazard to health and infrastructure. However, the complexity of these pathways typically makes this a challenging proposition, and uncertainties on the composition of mixed‐source sediments are often not reported. This study demonstrates that a quantitative fingerprinting method within the Bayesian Markov Chain Monte Carlo (MCMC) framework offers great potential for exploring the provenance and uncertainties associated with aeolian sands. > Eight samples were taken from dunes of the small (~58?km 2 ) Ashkzar erg, central Iran, and 49 from three distinct potential sediment sources in the surrounding area. These were analyzed for 61 tracers including 53 geochemical elements (trace, major and rare earth elements (REE)) and eight REE ratios. Kruskal–Wallis H‐tests and stepwise discriminant function analysis (DFA) allowed the identification of an optimum composite fingerprint based on six tracers (Rb, Sr, 87 Sr, (La/Yb) <sub>n</sub> , Ga and δCe), and a Bayesian mixing model was applied to derive the source apportionment estimates within an uncertainty framework. >There is substantial variation in the uncertainties in the fingerprinting results, with some samples yielding clear discrimination of components, and some with less clear fingerprints. Quaternary terraces and fans contribute the largest component to the dunes, but they are also the most extensive surrounding unit; clay flats and marls, however, contribute out of proportion to their small outcrop extent. The successful application of these methods to aeolian sediment deposits demonstrates their potential for providing quantitative estimates of aeolian sediment provenances in other mixed‐source arid settings, and may prove especially beneficial where sediment is derived from multiple sources, or where other methods of provenance (e.g. detrital zircon U–Pb dating) are not possible due to mineralogical constraints. Copyright ? 2017 John Wiley & Sons, Ltd. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><abstract xmlns =“http://www.wiley.com/namespaces/wiley”type =“main”xml：lang =“en”> <title type =“main”>抽象</ title> >识别沙子沉积Aeolian环境（例如DUNEFIELFS）的出处可以阐明沉积物途径和助势，并告知潜在的土地管理策略，其中风吹砂和灰尘是对健康和基础设施的危害。然而，这些途径的复杂性通常使得这种挑战性主张，并且通常没有报道混合源沉积物组合物的不确定性。该研究表明，贝叶斯马尔可夫链蒙特卡罗（MCMC）框架内的定量指纹识别方法提供了探索与风沙相关的出处和不确定性的巨大潜力。</ p> >八个样本从小的沙丘取出（〜58 km 2 </ sup>）Ashkzar erg，中央伊朗和49个周围地区三个不同的潜在沉积物。分析了61种示踪剂，包括53个地球化学元素（痕量，主要和稀土元素（REE））和八个REE比率。 Kruskal-Wallis H-Tests和逐步判别函数分析（DFA）允许识别基于六个示踪剂（RB，SR，<SUP> 87 </ SUP> SR，（LA / YB）<SUB> N的最佳复合指纹</ sub>，ga和Δce）和贝叶斯混合模型被应用于导出在不确定性框架内的源分摊估计。 </ p> >指纹识别结果中的不确定性存在显着变化，一些样品产生明显的组分辨别，并且有些样品较少的指纹。第四纪露台和粉丝为沙丘提供最大的组成部分，但它们也是最广泛的周边单位;然而，粘土公寓和玛尔斯与其小的露天度范围有贡献。这些方法的成功应用于海湾沉积物沉积物，证明了他们在其他混合源干旱环境中提供了在其他混合源干旱环境中提供的天气沉积物估计的可能性，并且可能尤其有益，其中沉积物来自多种来源，或其他物种方法（例如由于矿物学约束，不可能替代Zircon U-PB约会。版权？ 2017年John Wiley＆amp; SONS，LTD。</ p> </ abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-22319/'>《Earth Surface Processes and Landforms: The journal of the British Geomorphological Research Group》</a> <b style="margin: 0 2px;">|</b><span>2017年第14期</span><b style="margin: 0 2px;">|</b><span>共12页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Gholami Hamid&option=202" target="_blank" rel="nofollow">Gholami Hamid;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Telfer Matt W.&option=202" target="_blank" rel="nofollow">Telfer Matt W.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Blake William H.&option=202" target="_blank" rel="nofollow">Blake William H.;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Fathabadi Abolhassan&option=202" target="_blank" rel="nofollow">Fathabadi Abolhassan;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of Range and Watershed ManagementUniversity of HormozganBandar‐Abbas Hormozgan Iran;</p> <p>School of Geography Earth and Environmental SciencesPlymouth UniversityPlymouth Devon PL4 8AA UK;</p> <p>School of Geography Earth and Environmental SciencesPlymouth UniversityPlymouth Devon PL4 8AA UK;</p> <p>Department of Range and Watershed ManagementUniversity of Gonbad‐e‐KavoosGonbad‐e‐Kavoos Golestan Iran;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/163.html" title="地球物理学">地球物理学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=sand provenance&option=203" rel="nofollow">sand provenance;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=aeolian sediment&option=203" rel="nofollow">aeolian sediment;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Markov chain Monte Carlo&option=203" rel="nofollow">Markov chain Monte Carlo;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=fingerprinting&option=203" rel="nofollow">fingerprinting;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=uncertainty&option=203" rel="nofollow">uncertainty;</a> </p> <div class="translation"> 机译：沙子源;风沉积物;马尔可夫链蒙特卡罗;指纹;不确定性; 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D.,</a> <span>2012</span> </span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：在洛斯阿拉莫斯国家实验室，技术区54，G区对受污染沉积物的风能运输进行建模：对继承，干扰和未来气候的敏感性。</span> </p> </li> </ul> <ul style="display: none;"> <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/academic-journal-cn_journal-vibration-shock_thesis/0201212258654.html">基于混合高斯输出贝叶斯信念网络模型的设备退化状态识别与剩余使用寿命预测方法研究</a> <b>[J]</b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=张星辉&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 张星辉</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=康建设&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,康建设</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=赵劲松&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,赵劲松</a> <span> <a href="/journal-cn-9650/" target="_blank" rel="nofollow" class="tuijian_authcolor"> . 振动与冲击 </a> </span> <span> . 2014</span><span>,第008期</span> </span> </div> </li> <li> <div> <b>2. </b><a class="enjiyixqcontent" href="/academic-journal-cn_journal-computer-aided-design-computer-graphics_thesis/0201236293058.html">基于贝叶斯自组织映射和高斯混合模型的混合像元分解</a> <b>[J]</b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=刘力帆&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 刘力帆</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=王斌&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,王斌</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=张立明&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,张立明</a> <span> <a href="/journal-cn-12398/" target="_blank" rel="nofollow" class="tuijian_authcolor"> . 计算机辅助设计与图形学学报 </a> </span> <span> . 2007</span><span>,第011期</span> </span> </div> </li> <li> <div> <b>3. </b><a class="enjiyixqcontent" href="/academic-journal-cn_detail_thesis/0201293742000.html">空间面板数据混合效应模型的贝叶斯分位数回归</a> <b>[J]</b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=罗玉波&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 罗玉波</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=王轶凡&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,王轶凡</a> <span> <a href="/journal-cn-56841/" target="_blank" rel="nofollow" class="tuijian_authcolor"> . 统计与决策 </a> </span> <span> . 2021</span><span>,第8期</span> </span> </div> </li> <li> <div> <b>4. </b><a class="enjiyixqcontent" href="/academic-journal-cn_medical-equipment_thesis/0201277487797.html">基于贝叶斯分层混合模型的X线胸片图像病例分析</a> <b>[J]</b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=谢丽莉&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 谢丽莉</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=李振彰&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,李振彰</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=刘朝辉&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,刘朝辉</a> <span> <a href="/journal-cn-5560/" target="_blank" rel="nofollow" class="tuijian_authcolor"> . 医疗装备 </a> </span> <span> . 2020</span><span>,第005期</span> </span> </div> </li> <li> <div> <b>5. </b><a class="enjiyixqcontent" href="/academic-journal-cn_journal-frontiers-computer-science-technology_thesis/0201277960343.html">面向图像复原的分层贝叶斯局部高斯混合模型</a> <b>[J]</b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=张墨华&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 张墨华</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=彭建华&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">,彭建华</a> <span> <a href="/journal-cn-8902/" target="_blank" rel="nofollow" class="tuijian_authcolor"> . 计算机科学与探索 </a> </span> <span> . 2020</span><span>,第002期</span> </span> </div> </li> <li> <div> <b>6. </b><a class="enjiyixqcontent" href="/academic-conference-cn_meeting-11708_thesis/02022446457.html">基于贝叶斯及多模式串模糊匹配算法的不良短消息甄别混合模型</a> <b>[C]</b> <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Zhang Wenbo&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> . 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href="/patent-detail/061204290082.html">一种基于混合高斯模型和稀疏贝叶斯的主动学习分类方法</a> <b>[P]</b> . <span> 中国专利： CN110197286B </span> <span> . 2021.03.16</span> </div> </li> <li> <div> <b>2. </b><a class="enjiyixqcontent" href="/patent-detail/061204609727.html">一种基于贝叶斯混合模型的网络故障数据合成方法</a> <b>[P]</b> . <span> 中国专利： CN109951327B </span> <span> . 2021.08.20</span> </div> </li> <li> <div> <b>3. </b><a class="enjiyixqcontent" href="/patent-detail/06130449402146.html">BAYESIAN INFERENCE METHOD FOR MIXED MODEL AND RECORDING MEDIUM WITH RECORDED BAYESIAN INFERENCE PROGRAM FOR MIXED MODEL</a> <b>[P]</b> . <span> 外国专利：  JP2001202358A </span> <span> . 2001-07-27</span> </div> <p class="zwjiyix translation" style="max-width: initial;height: auto;word-break: break-all;white-space: initial;text-overflow: initial;overflow: initial;"> <span>机译：混合模型的贝叶斯推断方法和记录介质的混合贝叶斯推断程序 </span> </p> </li> <li> <div> <b>4. </b><a class="enjiyixqcontent" href="/patent-detail/06130422080949.html">REVERSE ANALYSIS METHOD FOR MIXED 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