金试样加工与分解的最新进展   总被引：5，自引：0，他引：5  

薛光 《黄金地质》1996,2(4):67-71

概述了金试样加工与分解的最新进展，对湿法分解方法进行了重点评述。  相似文献   

地球物理反问题中的随机反演理论     

周竹生  陆江南 《物探化探计算技术》1997,19(4):289-297

本文从最大后验概率密度观点出发，在数据噪音向量和待求模型向量为具有零均值的独立高斯随机过程的假设前提下，建立起了随机反演的非线性系统方程；给出了模型方差估计的函数表达式，并在文章最后，证明了反演解的稀疏性，即解释了随机反演的输出解的高分辨率特征。文章在最小二乘反演方法的基础上，发展并完善了随机反演方法的理论基础；揭示了随机反演方法与最小二乘反演方法之间的本质区别；阐述了随机反演方法的优越性，并指出了其广阔的应用前景。  相似文献   

地质样品中微量金银的在线分离富集方法     

齐菊锐  苏克  邹海峰  鲍长利  姜桂兰  齐清凤 《吉林大学学报(地球科学版)》1997,(4)

提出用ＶＳ－Ⅱ型强碱性阴离子交换纤维定量富集、硫脲解脱，流动注射在线分离富集—火焰原子吸收光谱法测定地质样品中痕量金银的分析方法。该方法检出限低，金为１．４μｇ／ｍＬ，银为０．３５μｇ／ｍＬ，相对标准偏差均小于２％，采样频率为１２０次／ｈ，用于地质样品中痕量金银的直接测定，结果令人满意。  相似文献   

近红外反射光谱学在岩石矿物研究中的应用Ⅱ.快速测定地质样品中有机质     

龙梅  裴世桥 《岩矿测试》2004,23(1):6-10

利用偏最小二乘法回归的多变量校正方式,建立了应用近红外反射光谱学方法无损快速测定各种地质样品中有机质的模型.设计了多重散射光校正、标准正常变量转换及导数光谱,扣除额外基线和重叠信号的影响,分离出与有机质含量有关的光谱信息.大多数地质样品的有机质近红外反射光谱估算结果与化学法符合.  相似文献   

发射光谱载体蒸馏法测定地质样品中微量硼铍锡银   总被引：2，自引：2，他引：2  

李滦宁  赵淑杰  王烨 《岩矿测试》2004,23(1):30-32

选择了适合的缓冲剂,利用载体蒸馏法将被测元素与基体元素分离,使Be转变为易挥发的氟化物与B、Sn、Ag同时蒸发;降低了谱线背景及元素检出限;加入内标元素Au、Bi,提高了方法的准确度和精密度;采用普通 3mm×3mm×0.7mm杯型电极,装样量少,操作简单。方法检出限(wB/10-6)分别可达B1.5、Be0.3、Sn0.8、Ag0.02;准确度(Δlog C)基本在-0.05～0.05,精密度(RSD,n=8)为2.9%～17%。  相似文献   

用于新生代定年的Ar-Ar法标准样品候选样品初测结果   总被引：5，自引：1，他引：5  

桑海清  丁林  戴檀谟 《矿物岩石地球化学通报》2004,23(1):24-27

Ar Ar法定年的特点是必须要有一套年龄从小到大的标准样。迄今用于新生代定年的国内标准样极少。为满足新生代矿物Ar Ar法定年的需要,初选了一个标准样候选者BT 1透长石。样品总重366g,粒级6080目,纯度100%,缩分为100瓶,每瓶3 66g。Ar Ar阶段加热法初测结果为:全部12个阶段给出的总气体年龄为30 8±0 9Ma,412阶段视年龄十分接近,年龄谱平坦,对应的39Ar析出量达96%,坪年龄为29 6±0 4Ma,等时年龄为29 6±0 6Ma,MSWD=1 01。40Ar/36Ar初始值为293 6±3 9,与尼尔值295 5相当。重复测定结果为:全部气体年龄为31 0±0 9Ma(全部9个阶段),坪年龄为29 5±0 4Ma,等时年龄为29 4±0 6Ma,MSWD=1 94,40Ar/36Ar初始值为282 2±6 3。这些结果表明,BT 1透长石不含过剩氩,作为新生代定年的Ar Ar法标准样品候选者是较为理想的。  相似文献   

Some effects of the memory kernel singularity on wave propagation and inversion in viscoelastic media – II. Inversion     

A. Ribodetti  A. Hanyga 《Geophysical Journal International》2004,158(2):426-442

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Geochemistry of Late Cretaceous （ 60 - 67 Ma） igneous activities in the Hebrides Terrace seamount （guyot） area, Scotland     

M.El-Tokhi M.Omran A.E-Muslem 《中国地球化学学报》2005,24(1):9-17

Tholeiitic basalts in various stages of alteration were dredged from Late Cretaceous volcanic rocks (60 -67 Ma) in the Hebrides Terrace seamount area in the Atlantic Ocean. These rocks are extrusive olivine basalts, including high- and low-Al basalts. High-Al basalts are depleted in MgO, CaO, Cr,Sc, V, St, Zr and enriched in TiO2, Na2O, Nb, Rb as compared with low-A1 basalts. Petrography and bulk-rock composition (major, trace and rare-earth elements) data defined clear tholeiitic suites displaying possible liquid lines of descent related to different degrees of crystal fractionation and partial melting.Isotopic dating of dredged samples gave the guyot an age of 60 - 67 Ma, in support of the assumption that it was formed during the Late Cretaceous.  相似文献   

Evaluating soil moisture estimation from ground-penetrating radar hyperbola fitting with respect to a systematic time-domain reflectometry data collection in a boreal podzolic agricultural field     

Chameera Illawathure  Gary Parkin  Sébastien Lambot  Lakshman Galagedara 《水文研究》2020,34(6):1428-1445

The upper 30 cm of the soil profile, which hosts the majority of the root biomass, can be considered as the shallow agricultural root zone of most temperate crops. The electromagnetic wave velocity in the soil obtained from reflection hyperbolas in ground-penetrating radar (GPR) data can be used to estimate soil moisture (SM). Finding shallow hyperbolas in a radargram and minimizing the subjective error associated with the hyperbola fitting are the main challenges in this approach. Nevertheless, we were motivated by the recent improvements of hyperbola fitting algorithms, which can reduce the subjective error and processing time. To overcome the difficulty of finding very shallow hyperbolas, we applied the hyperbola fitting method to reflections ranging from 27- to 50-cm depth using a 500-MHz centre-frequency GPR and compared the estimated moisture with vertically installed, 30-cm-long time-domain reflectometry (TDR) sensors. We also compared TDR and GPR sample areas in a 2-D plane using different GPR survey types and different hyperbola depths. SM measured with TDR and GPR were not significantly different according to Mann–Whitney's test. Our analyses showed that a root mean square error of 0.03 m³ m⁻³ was found between the two methods. In conclusion, the proposed method might be suitable to estimate SM with an acceptable accuracy within the root zone if the soil profile is fairly uniform within the application depth range.  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献