The first discovery of axinite in the Furong tin deposit,southern Hunan: An important boron -bearing mineral and its implications for tin mineralizationSCIEI北大核心CSCD

世界上绝大部分锡矿床均与富F、Cl、Li、B等挥发份的高分异花岗岩有关,前人对F、Cl、Li等组份在花岗质岩浆演化及锡成矿过程中的作用已有较为深入的认识,但对挥发份B关注较少。南岭中段的芙蓉超大型锡矿床是我国重要锡矿产地,我们首次在该矿床发现了硼硅酸盐矿物中罕见的斧石,为研究挥发份B对锡成矿的作用提供了机会。通过详细的岩石学、矿物学和地球化学研究,揭示了斧石的产出特征、晶体结构和化学成分特征,进而探讨了挥发份B与该区花岗岩成岩成矿的关系。芙蓉锡矿床新发现的斧石为丁香褐色、块状构造、硬度较大,显微镜下呈无色、浅黄色等,主要为自形尖劈状的楔形、长条状等自形结构、正高突起、干涉色较低,与符山石、萤石、石榴子石、榍石等矿物共生。电子探针分析表明,芙蓉矿床的斧石属铁斧石,其平均化学式为Ca_(2.22)(Fe_(0.52),Mn_(0.41),Mg_(0.13))1.06Al_(2.11)B_(0.72)Si_(3.98)O_(15.5)。结合前人研究成果,芙蓉矿床斧石、电气石、硼镁铁矿等富B矿物的广泛产出,指示了骑田岭花岗岩富含挥发份B;硅酸盐熔体中挥发份B的存在,能降低岩浆粘度,增加岩浆的结晶分异程度,延长岩浆寿命,进而有利于Sn等不相容元素在岩浆演化过程中向残余熔体富集。B与锡成矿的关系密切,岩浆中的B能提高源区Sn的萃取效率,增加原始岩浆中Sn的含量;B能形成锡的络合物,有利于锡的长距离运移;B与Sn具有相似的地球化学行为,在岩浆演化过程中,富集于残余熔体中,进一步演化后富集于成矿流体中。此外,在芙蓉锡矿床,随着含B矿物的不断被发现,表明该矿床可能具有寻找伴生硼矿的潜力。     

Distribution characteristics and factors influencing microbial communities in the core soils of a seawater intrusion area in Longkou City,China

Microbes live throughout the soil profile. Microbial communities in subsurface horizons are impacted by a saltwater–freshwater transition zone formed by seawater intrusion (SWI) in coastal regions. The main purpose of this study is to explore the changes in microbial communities within the soil profile because of SWI. The study characterizes the depth-dependent distributions of bacterial and archaeal communities through high-throughput sequencing of 16S rRNA gene amplicons by collecting surface soil and deep core samples at nine soil depths in Longkou City, China. The results showed that although microbial communities were considerably impacted by SWI in both horizontal and vertical domains, the extent of these effects was variable. The soil depth strongly influenced the microbial communities, and the microbial diversity and community structure were significantly different (p < 0.05) at various depths. Compared with SWI, soil depth was a greater influencing factor for microbial diversity and community structure. Furthermore, soil microbial community structure was closely related to the environmental conditions, among which the most significant environmental factors were soil depth, pH, organic carbon, and total nitrogen.

     

Uncertainty quantification of landslide runout motion considering soil interdependent anisotropy and fabric orientation

Landslides - Natural soils often exhibit an anisotropic fabric pattern as a result of soil deposition, weathering, or filling. This paper aims to investigate the effect of soil interdependent...     

Bayesian active learning for parameter calibration of landslide run-out models

Landslides - Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out models contain parameters that cannot...     

蒙特卡罗法在地热资源评价中的应用

通过印度尼西亚B地热田地质勘查和已有资料分析,参照中国地热行业勘查和资源评价相关规范,对比该地热田已有的资源评价结果;以热储法（体积法）为基本公式,运用蒙特卡罗（Monte Carlo）法进行预测,随机参数均为地热地质条件限定下的取值范围;最终以体积法估算结果作为评估资源量下限,获得待评价地热田的可发电量资源潜力,为该地热田的进一步开发提供可靠依据。相比传统方法,蒙特卡罗法更适用于地热勘查初级阶段的地热资源评价,具有预测范围合理、数据结果定量化、可提供概率分析置信区间等优点。     

WV-3遥感数据在滇西矿产资源开发与地质灾害调查中的应用

选择云南三江中南段高黎贡山之南作为研究对象,利用WorldView-3卫星数据高空间分辨率的特点,基于Skyline软件构建三维模拟场景,从不同视域范围、远近尺度、方位视角对解译目标进行全方位展示,获取了不同矿山开发及地质灾害的典型识别标志,进行矿产资源开发状况精细识别及地质灾害详细信息提取,解译出区内建筑石料用灰岩等5种非金属矿以及滑坡等3种类型地质灾害,为矿山管理及地质灾害调查提供快速精准的目标,提升工作效率和质量。     

Characterizing uncertainty in geotechnical design of energy piles based on Bayesian theorem

It is known that a lot of uncertainties are involved in geotechnical design of energy piles. In this paper, a Bayesian updating framework is presented to characterize those uncertainties. The load-transfer model is developed to predict the thermomechanical response of energy piles. Considering the cross-case variability of the uncertainty in the axial strains of pile, the global model bias is firstly calibrated by establishing a comprehensive database consisting of 12 energy pile cases. Furthermore, the uncertainty in input parameters is considered in the Bayesian updating of model bias in a specific case. The variability of the uncertain parameters is effectively reduced after updating. The coefficient of variation of prediction is decreased from 0.34 to 0.13. The present framework can well quantify uncertain factors and improve the accuracy and reliability of the prediction model.