Using multivariate statistical analysis of groundwater major cation and trace element concentrations to evaluate groundwater flow in a regional aquifer

Groundwater samples were collected from 11 springs in Ash Meadows National Wildlife Refuge in southern Nevada and seven springs from Death Valley National Park in eastern California. Concentrations of the major cations (Ca, Mg, Na and K) and 45 trace elements were determined in these groundwater samples. The resultant data were subjected to evaluation via the multivariate statistical technique principal components analysis (PCA), to investigate the chemical relationships between the Ash Meadows and Death Valley spring waters, to evaluate whether the results of the PCA support those of previous hydrogeological and isotopic studies and to determine if PCA can be used to help delineate potential groundwater flow patterns based on the chemical compositions of groundwaters. The results of the PCA indicated that groundwaters from the regional Paleozoic carbonate aquifers (all of the Ash Meadows springs and four springs from the Furnace Creek region of Death Valley) exhibited strong statistical associations, whereas other Death Valley groundwaters were chemically different. The results of the PCA support earlier studies, where potentiometric head levels, δ¹⁸O and δD, geological relationships and rare earth element data were used to evaluate groundwater flow, which suggest groundwater flows from Ash Meadows to the Furnace Creek springs in Death Valley. The PCA suggests that Furnace Creek groundwaters are moderately concentrated Ash Meadows groundwater, reflecting longer aquifer residence times for the Furnace Creek groundwaters. Moreover, PCA indicates that groundwater may flow from springs in the region surrounding Scotty's Castle in Death Valley National Park, to a spring discharging on the valley floor. The study indicates that PCA may provide rapid and relatively cost‐effective methods to assess possible groundwater flow regimes in systems that have not been previously investigated. Copyright © 1999 John Wiley & Sons, Ltd.     

Intensity measures for probabilistic assessment of non‐structural components acceleration demand

A fundamental issue in the framework of seismic probabilistic risk analysis is the choice of ground motion intensity measures (IMs). Based on the floor response spectrum method, the present contribution focuses on the ability of IMs to predict non‐structural components (NSCs) horizontal acceleration demand. A large panel of IMs is examined and a new IM, namely equipment relative average spectral acceleration (E‐ASA_R), is proposed for the purpose of NSCs acceleration demand prediction. The IMs efficiency and sufficiency comparisons are based on (i) the use of a large dataset of recorded earthquake ground motions; (ii) numerical analyses performed on three‐dimensional numerical models, representing actual structural wall and frame buildings; and (iii) systematic statistical analysis of the results. From the comparative study, the herein introduced E‐ASA_R shows high efficiency with respect to the estimation of maximum floor response spectra ordinates. Such efficiency is particularly remarkable in the case of structural wall buildings. Besides, the sufficiency and the simple formulation allowing the use of existing ground motion prediction models make the E‐ASA_R a promising IMs for seismic probabilistic risk assessment. Copyright © 2015 John Wiley & Sons, Ltd.     

磁化率对大地电磁响应的影响研究

本文推导了物质的磁化率大于自由空间的情况下的二维大地电磁(MT)正演计算公式. 利用有限差分法实现了其正演计算,获得了电磁场的分布. 详细地讨论了磁化率对MT响应的影响,证实了磁化率主要影响磁场信号的同相分量,而对磁场信号的异相分量和电场信号的影响比较弱. 当磁化率κ≥001时,在某些低频点的视电阻率值可以增加5Ωｍ左右,并且随着磁化率增加,这种增加的幅度显著增大;而对阻抗相位的影响则非常小. 新的认识为高磁化率物质存在的地区观测到的MT数据解释提供一种新的研究思路.     

全风化花岗岩化学及矿物成份在全土和粘粒中的不同表征

对全风化花岗岩化学和矿物成份的测试常用全土和粘粒部分(粒径<2μm)分别进行。业已发现两者由于物质组成和结构特征的差异,对土工程地质性质所起作用不同。本文给出了6个全风化花岗岩样品的全土和粘粒部分筛分法和移液管法测定粒度成份,用X荧光光谱仪做全量化学分析,及XRD矿物学分析的结果。对比这两部分数据发现,属于含砾土的这些样品化学成份的变化同矿物一致,尤其是同粘土矿物成份含量变化很一致。同全岩相比,粘粒化学成份中的SiO₂减少了近50%,K₂O含量也降低,而Al₂O₃、Fe₂O₃和H₂O⁺都明显升高。粘粒中氧化物相对含量升高者居多;全岩矿物成份以石英、粘土矿物和长石为主,粘粒中埃洛石和高岭石占大多数,其次为伊利石;粒度成份、化学成份和矿物含量相关性比较明显的是:石英和角砾正相关,和砂粒负相关,长石和砂粒正相关,粘土矿物含量和Al₂O₃、烧失量(LOI)和埃洛石含量正相关。     

抗差岭型组合主成分估计及误差影响

本文从有偏估计类中的岭型组合主成分估计出发 ,结合抗差估计理论 ,利用抗差M估计模型 ,提出了一种新的抗差有偏估计法———抗差岭型组合主成分估计。推导了平差参数的抗差岭型组合主成分估计解 ,以及平差参数的验后精度和误差影响函数。计算结果表明 ,抗差岭型组合主成分估计不但能克服法方程系数阵病态性的影响 ,而且能有效地抵制观测值中精差的异常干扰 ,使参数的解更为准确可靠     

基于气候变化和人类活动影响的土地利用分析——以新疆阿克苏河流域绿洲为例

以1990、 2000、 2010年3期Landsat TM影像为土地利用基础数据, 结合1990年以来的社会经济资料和反映气候变化的气温、 降水数据, 对1990年以来阿克苏河流域绿洲土地利用变化进行了分析. 结果表明: 1990-2010年期间, 耕地、 建设用地以单向转入为主, 耕地对绿洲"扩张"贡献最大, 前期(1990-2000年)的贡献率为129.1%, 后期(2001-2010年)的贡献率提高到142.5%; 草地、 水域以单向转出为主, 草地对绿洲"萎缩"贡献最大, 前期的贡献率是-39.8%, 后期是-34.8%; 其次是水域, 前期对绿洲的贡献率是-1%, 后期是-9.3%; 河漫滩、 林地、 湿地在前期对绿洲的"扩张"贡献率分别是7.5%、 0.3%和5.1%, 后期对绿洲的"萎缩"贡献率分别是-2.4%、 -0.02%和-0.4%. 近20 a来流域绿洲规模整体上持续扩张. 通过分析绿洲土地利用驱动力系统可以看出, 绿洲土地利用类型变化主要受人类活动影响, 气候变化起基础性作用.     

塔里木盆地腹地晚新生代沉积序列的超细粒组分记录及其古气候意义

对红白山剖面的粒度分析表明,塔里木盆地腹地晚新生代沉积物的粒度曲线中普遍存在一个超细粒组分。其众数粒径分布比较稳定,平均为0.87μm,含量变化介于0.3%～10%,在不同成因的沉积物中和在剖面上都具有明显的变化规律。对同一岩性段而言,超细粒组分含量在风成砂中最低,在黄土中较低,在河流相沉积中较高,在湖相泥岩中最高。尽管受岩性变化的影响比较显著,但不同成因沉积物的超细粒组分含量在剖面上的变化基本一致,暗示了其长周期变化受同一驱动因子控制。化学风化和/或成壤作用强度可能起着关键作用。红白山剖面超细粒组分含量在2.8Ma的快速降低指示了化学风化和/或成壤作用强度的显著减弱,进而反映了塔里木盆地腹地干旱化的显著加强。     

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman,near‐infrared and green signals

Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (?4·2 cm verusu ?4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (?3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.     

PLANT ADAPTATION,FIRM ENVIRONMENTS AND LOCATION ANALYSIS

Principal components analysis of fracture trace and sinkhole characteristics near 33 wells drawing water from the Ocala aquifer identified three hydrogeologically significant components. Multiple regression analysis using these components as independent variables showed that the flow of water to a well bore is influenced in order of importance by (1) proximity to a zone of high secondary permeability; (2) average aquifer transmissivity near the well; and (3) degree of cavity development in the closest zone of high secondary permeability.