内蒙古库伦旗秦家沟土壤地球化学特征及找矿方向

本文通过统计秦家沟土壤样品多元素地球化学参数,推测可能的矿源层和富集成矿作用,分析Grd数据对比图,分析各元素高值区的分布特征,根据相关系数（λ）矩阵的因子分析结果,得到了2个元素组合,推测出本区的主成矿元素和伴生元素,根据R型聚类分析谱系图的统计结果,推测出了本区可能存在的中高温元素组合类型和中低温元素组合类型,确定了本区有利的成矿元素组合类型,圈定了组合异常,通过归一化Grd数据图和组合异常对比,验证异常的合理性和有效性。通过施工地表工程,对所圈定的异常进行了查证,取得了良好的找矿效果,揭示了本区所蕴藏的成矿信息,指明了本区今后的找矿方向,为更好地指导本区下一步勘查工作提供了依据。     

一种基于贝叶斯理论的区域斜坡稳定性评价模型

本文结合滑坡物理模型和统计模型的优点,针对小流域滑坡稳定性分析,建立了一种基于贝叶斯理论的区域斜坡稳定性评价模型。该模型主要采用灾害自身信息来修正原始模型中的参数,解决了区域稳定性评价中参数难以确定的问题。该方法首先设定模型的初始参数分布,然后利用采样点雨前和雨后稳定性不同的信息建立验证方程,再根据马尔科夫链蒙特卡罗模拟和贝叶斯方法确定最终的参数分布,进而得出区域稳定性分布。应用该模型对福建省蔡源小流域的6 13滑坡群发性事件进行分析。结果显示,蔡源小流域地区的无量纲黏聚系数C为0.028,有效摩擦角为16.7,土壤的导水系数T和降雨量q的比值为529.026m,可能不稳定地区和不稳定地区达到76.0%。该模型利用历史灾害数据自动模拟出合适的参数,对区域滑坡稳定性评价具有重要意义。     

土壤水分对土壤参数的敏感性及其参数优化方法研究

利用2008年1月1日至2009年9月31日黑河流域阿柔冻融观测站的气象和土壤水分数据,采用基于方差的多参数敏感性分析方法研究通用陆面模型(Common Land Model,CoLM)模拟的土壤水分对土壤质地(砂土和黏土)的敏感性,进而采用SCE-UA参数优化算法分别优化土壤质地和土壤水力参数,分析不同优化策略对土壤水分模拟结果的影响。研究结果表明,浅层土壤水分对土壤质地较为敏感,敏感性系数达到了0.45以上,并且砂土含量对土壤水分的影响更为显著;利用SCE-UA算法优化土壤质地或土壤水力参数都可以有效地提高土壤水分的模拟精度,优化土壤水力参数易产生"异参同效"现象,而优化土壤质地能够使土壤水力参数的取值范围更加合理。     

液体闪烁测量法测定土壤样品中的镭

本文介绍一种新的分析方法——壤中镭水浸泡液体闪烁测定法。将50克80目土样置于500毫升磨口玻璃瓶中,加水浸泡,用闪烁液萃取氡,以液体闪烁计数法积分计数。该法灵敏度高,当取样50克,计数100秒时镭含量探测极限为5×10~(-13)克/克。方法简单快速,(每人每天可完成40个样品),重现性好,(相对偏差均在20％以下),成本低廉,干扰少(能区分~(222)Rn和~(220)Rn),易于推广(不需复杂的仪器)。经野外试验得到较好的效果。     

Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats,India using RUSLE and GIS

Revised Universal Soil Loss Equation(RUSLE) model coupled with transport limited sediment delivery(TLSD) function was used to predict the longtime average annual soil loss, and to identify the critical erosion-/deposition-prone areas in a tropical mountain river basin, viz., Muthirapuzha River Basin(MRB; area=271.75 km~2), in the southern Western Ghats, India. Mean gross soil erosion in MRB is 14.36 t ha~(-1) yr~(-1), whereas mean net soil erosion(i.e., gross erosion-deposition) is only 3.60 t ha~(-1) yr~(-1)(i.e., roughly 25% of the gross erosion). Majority of the basin area(~86%) experiences only slight erosion(5 t ha~(-1) yr~(-1)), and nearly 3% of the area functions as depositional environment for the eroded sediments(e.g., the terraces of stream reaches, the gentle plains as well as the foot slopes of the plateau scarps and the terrain with concordant summits). Although mean gross soil erosion rates in the natural vegetation belts are relatively higher, compared to agriculture, settlement/built-up areas and tea plantation, the sediment transport efficiency in agricultural areas and tea plantation is significantly high,reflecting the role of human activities on accelerated soil erosion. In MRB, on a mean basis, 0.42 t of soil organic carbon(SOC) content is being eroded per hectare annually, and SOC loss from the 4th order subbasins shows considerable differences, mainly due to the spatial variability in the gross soil erosion rates among the sub-basins. The quantitative results, on soil erosion and deposition, modelled using RUSLE and TLSD, are expected to be beneficial while formulating comprehensive land management strategies for reducing the extent of soil degradation in tropical mountain river basins.     

http://www.sciencedirect.com/science/article/pii/S1674987111001034

A comprehensive methodology that integrates Revised Universal Soil Loss Equation（RUSLE） model and Geographic Information System（GIS） techniques was adopted to determine the soil erosion vulnerability of a forested mountainous sub-watershed in Kerala,India.The spatial pattern of annual soil erosion rate was obtained by integrating geo-environmental variables in a raster based GIS method.GIS data layers including,rainfall erosivity（R）,soil erodability（K）,slope length and steepness（LS）,cover management （C） and conservation practice（P） factors were computed to determine their effects on average annual soil loss in the area.The resultant map of annual soil erosion shows a maximum soil loss of 17.73 t h^-1 y^-1 with a close relation to grass land areas,degraded forests and deciduous forests on the steep side-slopes（with high LS ）.The spatial erosion maps generated with RUSLE method and GIS can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive mountainous areas.     

陇东地区水土流失与地质作用初步分析

陇东地区是黄河上游水土流失严重区 ,也是国家重点治理的地区之一。由于特殊的岩土体的存在 ,加重了这一地区水土流失作用的危害性。本文通过水土流失现象与地质作用的关系进行偿试性探讨 ,旨在从中找到防治水土流失的有效措施 ,开展治理工作     

内蒙古中西部地区土壤水分对沙尘暴的贡献

以2001年4月至2002年6月内蒙中部地区逐时观测的土壤水分资料为基础，论证了该区日土壤水分和日平均风速的变化规律，统计分析了日平均风速与土壤水分对沙尘暴的成生综合贡献。得出的主要结论是：①该地区沙尘暴发生时日平均风速的最小值是3．5m／s。如果日平均风速大于8．0m／s，预示着有沙尘暴的发生。②在平均风速大于3.5m／s的条件下，观测样本中18．4％日数发生沙尘暴，而81．6％日数没有发生。说明大风的天气条件不应当被视其为沙尘暴的唯一重要的因子。③在同一地点、同样的风力条件下，在沙尘暴发生时，日平均风速与日平均土壤水分呈明显的反相关变化；而它们的反相关关系不明显时，沙尘暴就很少发生。     

根分泌的化感物质及其对土壤生物产生的影响

植物根系分泌物包含多种功能次生物质,其中化感物质是具有重要生理生化功能的一类物质,这些化感物质释放到根际,能够对土壤生物产生影响。现已发现,根系是化感物质进入环境的重要通道。因此,研究根分泌的化感物质具有重要的理论和实践意义。总结了根分泌化感物质的种类及其化感效应,论述了化感物质在土壤中的迁移、转化及影响因素,分析了生物和非生物因素与根系分泌化感物质之间的关系。此外,根系分泌化感物质的研究手段对于所取得的研究结果至关重要,其中根分泌物收集系统（CRETS）是收集根分泌化感物质的常用而且可行的方法之一。对于根分泌化感物质的分离鉴定技术有多种,可根据需要选择适宜的分离方法。还重点列举了一些作物根分泌的化感物质对土壤生物产生的直接和间接影响,阐明了根分泌的化感物质在土壤中所起的重要化感作用。根分泌物化感作用的研究已成为土壤生态学领域的热点与前沿课题,自然条件下原位收集鉴定植物根系分泌物中的化感物质等诸多问题是该领域今后的研究重点。     

Stratigraphic delineation by three-dimensional clustering of piezocone data

Piezocone soundings are a fast and economical approach for geotechnical site characterization, providing three separate and continuous channels of data with depth, including: tip resistance q _T, porewater pressure u ₂ and sleeve friction f _s. Literally hundreds to thousands of data points are collected by a single sounding. Since these readings are functions of both soil type and soil behaviour, they can be used for the delineation of soil stratigraphy.

One way to process large amounts of data involves clustering. Cluster analysis is an efficient statistical way to analyse the stratigraphic vertical profiling of geomaterials and means to detect the inherent similarity between data sets and group them together. Clustering in previous geotechnical research was based on only two channels of piezocone data (q _T and u₂). The method works well for soils that are under the groundwater table and was applied to soundings in clay deposits.

In the present paper, a new cluster analysis approach is developed based on all three channels of data, thus extending the method to soils above the water table and applicable to sands, silts, and clays. Example soil profiles derived by three-channel cluster analysis are presented herein and compared with conventional soil boring and sampling data.