对电感耦合等离子体质谱法测定的地下水中 B元素不确定度的评定

采用不确定度连续传递模型,以x、y的相对误差为权重进行双误差曲线回归,对电感耦合等离子体质谱法测定的地下水中B元素的不确定度进行评定。其不确定度分量主要包括标准储备溶液、配制标准系列溶液、重复性测量和曲线拟合产生的不确定度。研究表明,对于B浓度较高的样品,采用双误差拟合方式得到的不确定度结果比常规拟合方法(最小二乘法直线拟合)更为准确。     

全过程联合校正方法在饶河流域洪水预报中的应用研究

为提高饶河流域洪水预报精度,将全过程联合校正(EPJC)方法与三水源新安江(XAJ)模型结合,按一定比例划分洪水预报总误差为各过程误差,基于系统响应理论反演得到面雨量计算误差和模型参数误差,重新输入流域水文模型正演得出校正后的洪水过程。选取洪峰流量相对误差、峰现时间绝对误差、径流深相对误差和确定性系数作为模型评价指标,量化XAJ预报与EPJC校正结果,并与传统的动态系统响应曲线(DSRC)方法进行对比。结果表明,EPJC能够考虑全过程误差,与实际误差分布情况更相符,可提升饶河流域的洪水作业预报精度,值得进一步研究与推广。     

双误差回归模型在ICP-MS测定饮用水中锶的不确定度评定的简化应用

在计算电感耦合等离子体质谱法测定饮用水中锶的浓度(x)时,由于标准系列配制和仪器检测过程中信号(y)漂移产生的不确定度会传递给最终的计算结果。普通的一次或多次线性拟合结果不能真实地反映对于x、y值都含误差的数据拟合情况。文章对标准曲线进行了双误差回归计算,从双误差回归线性方程推导出校准曲线拟合过程产生不确定度的计算公式,建立了方法检出限与曲线拟合参数(x、y、曲线截距和斜率)及其相关不确定度之间的关系式,依据误差连续传递公式及不确定度分量计算公式简化了合成不确定度的表达式。从合成不确定度计算公式中可得出,水样中锶含量的浓度越低,其对应的不确定度越大;标准系列配制过程不细致、仪器灵敏度低,方法检出限越差,与实际测试情况符合。     

对氨基二甲基苯胺比色法测定的地下水中硫化物 不确定度的评定

采用不确定度连续传递模型对对氨基二甲基苯胺分光光度法测定的地下水中硫化物的不确定度进行了评定,在标准曲线拟合这一关键步骤上采用双误差曲线拟合,并与最小二乘法的拟合结果进行比较。结果表明,对于含较高浓度硫化物的水样,由双误差拟合出来的结果比最小二乘法拟合的结果更为准确。在采用双误差曲线拟合对实际样品进行定量时,应选择合适浓度范围内的标准曲线,使测定浓度落在标准曲线的较高浓度部分,使结果更为准确、合理。     

皮尔逊-Ⅲ型分布曲线的变步长数值积分

通过对皮尔逊-Ⅲ型曲线数值积分的研究,提出了一种新的积分方法——事先确定误差和变步长积分法.其主要思想是先将皮尔逊-Ⅲ型分布曲线的广义积分转换为伽玛函数和常义积分,利用伽玛函数的递推公式和逼近公式计算出伽玛函数值,然后根据预定容许的相对误差和伽玛函数值确定绝对误差,再利用绝对误差确定基本步长,最后建立步长变动函数,使数值积分的步长按照抛物线规律自动增加,同时,充分考虑参数的适应性,以解决小参数收敛慢和大参数数据溢出问题.测试试验结果表明:事先确定误差免去了数值积分的试算过程,变步长积分能显著节省计算机的运行时间,且具有很宽的参数适应范围,在水利工程设计中具有较大的使用价值.     

水库洪量模拟预报误差分析

根据水库洪量模拟预报误差评定的方法,按照预报方案进行预报操作,将得出的预报值与实测值进行对照,计算出绝对误差与相对误差,然后统计在各种允许误差条件下的合格率,得到在一定合格率要求下的可能产生的相对误差,或找到达到一定的允许误差条件下的预报合格率,为提高水库模拟预报的精度提供技术支撑。     

关中西部眉县城区地热井回灌试验Q-p曲线方程探究

地热井回灌中,明确回灌压力(p,回灌压力水头)与回灌量(Q)的关系对区域地热绿色可持续开发极为关键。 为明确这个关系,以关中西部眉县城区G102井回灌试验先期有关数据为基础,对其进行优化处理,确定3组数据。依据该数据绘制Q-p曲线图,基于压水试验5类曲线特征,判断曲线图可能为抛物线型、对数型及幂函数型。由此,绘制了3类曲线图,差分法判断曲线类型。在判断结果的基础上,应用均衡误差法获得对应方程的参数,并依据先期回灌量数据平均值的最大偏差,确定回灌量允许最大绝对误差范围,最后经过后期实例验证及误差分析。结果表明:(1) 在研究区域,此方法所推得方程计算结果与实际相比未超出回灌量允许的绝对误差范围,且相对误差偏差小,可以满足回灌试验中对回灌量的评估。(2) 相对误差具有先逐渐变小后变大的特征,出现这种特征的原因与储层的饱和度、压力及非均质岩体等因素有关,超出了方程所包含的约束。(3) 结合研究区其他回灌井试验情况,确定该区域主要回灌地层为中元古界宽坪群时,Q-p曲线图为幂函数类型。该研究成果可为下一步该区域地热资源的有效开发提供一定的科学依据。      

频域反射分析法测定土壤含水率标定试验研究

对滑坡体土壤含水率进行长期和连续的监测,有利于对滑坡体的变形分析和安全预警。为了提高频域反射分析法（FDR法）测量土壤含水率的精度,通过室内标定和现场标定相结合的方式,对使用FDR法测定野外现场滑坡土壤含水率进行了标定。通过对比分析FDR法和烘干法测得的土壤含水率数据,探讨电压信号、含水率、绝对误差和相对误差之间的对应关系,并建立函数模型。试验结果表明:FDR法测定的土壤含水率较烘干法偏高;通过线性、多次曲线及指数形式对试验数据进行拟合和回归分析,最终确定四次曲线作为标定方式,标定后测得的土壤含水率精度明显提高,平均绝对误差可控制在2%以内。该方法可用于类似条件下FDR法测定土壤含水率的标定。     

洪水预报自回归实时校正多步外延方法研究

自回归模型在洪水预报实时校正中应用广泛。针对自回归模型进行连续多时段校正时中间误差系列缺失问题,提出一种基于历史洪水预报误差系列的样本重组自回归外延方法,以淮河流域王家坝断面为背景,选用洪量相对误差、洪峰相对误差、峰滞时间和确定性系数四个指标开展校正效果评估,并与时程递推外延方法对比。结果表明:样本重组外延方法可以提升洪水预报精度,延长洪水预报有效预见期,特别在降低洪量误差和提高洪水过程的拟合精度上优势更为显著。同时,该方法泛化能力较强,具有实用价值。     

基于Copula函数的CFG桩复合地基载荷-变形曲线的概率分析

CFG桩(cement-fly ash-gravel pile)复合地基是一种重要的地基处理形式,在日益增加的大面积住宅和商业开发中作用越来越突出,然而该种桩型的加卸荷-沉降变形特性仍然需深入研究,尤其在概率评估方面。根据北京星光影视股份有限公司生产科研基地项目工地中的21根CFG桩单桩静载试验和32个复合地基静载试验的原位加卸载测试成果,采用两参数的双曲线或幂曲线回归拟合了每一条加荷-变形曲线。由于土体的内在各向异性和其强度的变异性,评估整个场地的加荷-变形曲线时,其回归参数表现出了较大的离散性。将一个场地的多组回归参数组成一个随机向量,其加载-位移曲线的不确定性可由简单的两变量随机向量体现,引入双变量联结函数（Copula）描述随机回归参数间的相依性。最后,考虑正常使用极限状态,采用基于Copula函数的模拟模型计算了CFG桩复合地基的可靠度指标。研究结果有助于改进CFG桩复合地基的概率设计与评估。     

Granitoids around the Malanjkhand copper deposit: Types and age relationship

On the basis of field relations, petrography and chemistry, three types of granitoids are recognized at Malanjkhand in and around the copper deposit over an area of about 200 km². These are (i) a fine grained ‘leucogranite’ of restricted occurrence in the surrounding area (Gr-I); (ii) coarse-grained, grey in most parts, gneissose granitoid of regional extension (Gr-II); and (iii) the pink-feldspar bearing massive type hosting the mineralization with occasional representatives in the surrounding country (Gr-III). Gr-I comes out as a distinct entity on the basis of cross-cutting relation and mineralogical and chemical composition, the Rb-Sr whole rock isochron also giving a younger age than the other two groups irrespective of the regression model used. Gr-II comes out as the oldest unit but its age relationship with Gr-III cannot be established unequivocally. An uncorrelated error regression model establishes the age relationship as Gr-I<Gr-III<Gr-II, whereas a two-error regression model establishes temporal closeness between Gr-II and III. The term ‘granitoids’ is being used broadly to include rocks ranging in composition from alkali-granite to tonalite.     

Isotherms for the sorption of zinc and copper onto kaolinite: comparison of various error functions

In this research, the equilibrium sorption of Zn(II) and Cu(II) by kaolinite was explained using the Freundlich, Langmuir and Redlich–Peterson isotherms, via both linear and non-linear regression analyses. In the case of non-linear regression method, the best-fitting model was evaluated using six different error functions, namely coefficient of determination (r ²), hybrid fractional error function (HYBRID), Marquardt’s percent standard deviation (MPSD), average relative error (ARE), sum of the errors squared (SSE) and sum of the absolute errors (EABS). The examination of error estimation methods showed that the Langmuir model provides the best fit for the experimental equilibrium data for both linear and non-linear regression analyses. The SSE function was found to be a better option to minimize the error distribution between the experimental equilibrium data and predicted two-parameter isotherms. In the case of three-parameter isotherm, HYBRID was found to be the best error function to minimize the error distribution structure between experimental equilibrium data and theoretical isotherms. Non-linear method was found to be more appropriate method for estimating the isotherm parameters.     

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

The unconfined compressive strength (UCS) of intact rocks is an important geotechnical parameter for engineering applications. Determining UCS using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The objective of study is to select the explanatory variables (predictors) from a subset of mineralogical and index properties of the samples, based on all possible regression technique, and to prepare a prediction model of UCS using artificial neural networks (ANN). As a result of all possible regression, the total porosity and P-wave velocity in the solid part of the sample were determined as the inputs for the Levenberg–Marquardt algorithm based ANN (LM-ANN). The performance of the LM-ANN model was compared with the multiple linear regression (REG) model. When training and testing results of the outputs of the LM-ANN and REG models were examined in terms of the favorite statistical criteria, which are the determination coefficient, adjusted determination coefficient, root mean square error and variance account factor, the results of LM-ANN model were more accurate. In addition to these statistical criteria, the non-parametric Mann–Whitney U test, as an alternative to the Student’s t test, was used for comparing the homogeneities of predicted values. When all the statistics had been investigated, it was seen that the LM-ANN that has been developed, was a successful tool which was capable of UCS prediction.     

Suspended sediment load prediction of river systems: GEP approach

This study presents gene expression programming (GEP), an extension of genetic programming, as an alternative approach to modeling the suspended sediment load relationship for the three Malaysian rivers. In this study, adaptive neuro-fuzzy inference system (ANFIS), regression model, and GEP approaches were developed to predict suspended load in three Malaysian rivers: Muda River, Langat River, and Kurau River [ANFIS (R ²?=?0.93, root mean square error (RMSE)?=?3.19, and average error (AE)?=?1.12) and regression model (R ²?=?0.63, RMSE?=?13.96, and AE?=?12.69)]. Additionally, the explicit formulations of the developed GEP models are presented (R ²?=?0.88, RMSE?=?5.19, and AE?=?6.5). The performance of the GEP model was found to be acceptable compare to ANFIS and better than the conventional models.     

FEM Simulations of Granular Matter Behaviour Under Triaxial Tests

This article presents the results of a numerical simulation carried out from a series of standard triaxial tests conducted on limestone sand from a quarry situated in Algiers (center of Algeria). The main objective of the investigation is to obtain from a numerical analysis a simple soil model to represent the behaviour of the studied material. The 2D Plaxis program is used in this study with the introduction of a model based on the work of Brinkgreve et al. (in: Benz T, Nordal S (eds) Numerical methods in geotechnical engineering, CRC Press, Boca Raton, 2010). Corrections were made to the formulas used, which are based on the relative density, in order to improve the quality of the results in terms of soil behaviour, mechanical strength and deformation. The comparison of the experimental and numerical results for the Mohr–Coulomb model gives a considerable appreciation on the deformation and resistance. However, it fails to represent properly the stress–strain curve. The use of Duncan and Chang model (Hardening Soil Model-HSM) leads to an underestimation of the resistance characteristics (values of the friction angles) with an overall error of 7.98%. Our work consists of the application of two corrections to the HSM model. The first correction is based on the work of Brinkgreve et al. (2010), which focused on the common parameters between the HSM and the HSSM. An overestimation of the deformations was observed with an overall error of 155.96%. The second step consists of canceling the correction of the elastic modules (\(E_{50}^{ref}\), \(E_{oed}^{ref}\)) which reduces the error to 1.53%.

     

Mapping mineral prospectivity using an extreme learning machine regression

In this research, we conduct a case study of mapping polymetallic prospectivity using an extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer served as hardware platform. Almeida's Python program was used to construct the ELM regression model to map polymetallic prospectivity of the Lalingzaohuo district in Qinghai Province in China. Based on geologic, metallogenic, and statistical analyses of the study area, one target and eight predictor map patterns and two training sets were then used to train the ELM regression and logistic regression models. ELM regression modeling using the two training sets spends 61.4 s and 65.9 s; whereas the logistic regression modeling using the two training sets spends 1704.0 s and 1628.0 s. The four trained regression models were used to map polymetallic prospectivity. Based on the polymetallic prospectivity predicted by each model, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was estimated. The ROC curves show that the two ELM-regression-based models somewhat dominate the two logistic-regression-based models over the ROC performance space; and the AUC values indicate that the overall performances of the two ELM-regression-based models are somewhat better than those of the two logistic-regression-based models. Hence, the ELM-regression-based models slightly outperform the logistic-regression-based models in mapping polymetallic prospectivity. Polymetallic targets were optimally delineated by using the Youden index to maximize spatial association between the delineated polymetallic targets and the discovered polymetallic deposits. The polymetallic targets predicted by the two ELM-regression-based models occupy lower percentage of the study area (2.66–2.68%) compared to those predicted by the two logistic-regression-based models (4.96%) but contain the same percentage of the discovered polymetallic deposits (82%). Therefore, the ELM regression is a useful fast-learning data-driven model that slightly outperforms the widely used logistic regression model in mapping mineral prospectivity. The case study reveals that the magmatic complexes, which intruded into the Baishahe Formation of the Paleoproterozoic Jinshuikou Group or the Carboniferous Dagangou and Shiguaizi Formations, and which were controlled by northwest-western/east-western trending deep faults, are critical for polymetallic mineralization and need to be paid much attention to in future mineral exploration in the study area.     

The solubility of olivine in basaltic liquids: an ionic model

A simple ionic model which describes the solution of the forsterite component of olivine in silicate liquids is reported. The melting relation is represented: (Mg₂SiO₄)_ol = 2(Mg²⁺)_L + (SiO₄^4?_L and is extended to all silicate liquids by normalizing their compositions to 4 oxygens. At 1 bar, the temperature at which olivine is in equilibrium with any alkali-depleted basaltic composition can be calculated to within ±30°C. This error is increased considerably when applied to terrestrial basalts which contain several weight percent alkalis. Alkalis interfere with the equilibrium by generating strongly repulsive interionic forces which can be crudely modelled in a manner consistent with constraints imposed by regular solution theory.The model quantifies the reduced activity of SiO₄^4? monomers due to increasing SiO₂ concentrations in the melt. This is a consequence of polymerization which does not appear to operate gradually over the entire spectrum of mafic and ultramafic compositions. The coordination of alumina in melts which precipitate olivine only appears to be dominantly octahedral. Titanium acts as a polymerizing agent by interconnecting previously isolated SiO₄^4? monomers. Calcium associated with normative diopside tends to exhibit small but perceptible repulsive forces involving Mg²⁺.     

Integrating intensity–duration-based rainfall threshold and antecedent rainfall-based probability estimate towards generating early warning for rainfall-induced landslides in parts of the Garhwal Himalaya,India

In order to generate early warning for landslides, it is necessary to address the spatial and temporal aspects of slope failure. The present study deals with the temporal dimension of slope failures taking into account the most widespread and frequent triggering factor, i.e. rainfall, along the National Highway-58 from Rishikesh to Mana in the Garhwal Himalaya, India. Using the post-processed three-hourly rainfall intensity and duration values from the Tropical Rainfall Measuring Mission-based Multi-satellite Precipitation Analysis and the time-tagged landslide records along this route, an intensity–duration (I–D)-based threshold has been derived as I?=?58.7D ^?1.12 for the rainfall-triggered landslides. The validation of the I–D threshold has shown 81.6 % accuracy for landslides which occurred in 2005 and 2006. From this result, it can be inferred that landslides in the study area can be initiated by continuous rainfall of over 12 h with about 4-mm/h intensity. Using the mean annual precipitation, a normalized intensity–duration relation of NI?=?0.0612D ^?1.17 has also been derived. In order to account for the influence of the antecedent rainfall in slope failure initiation, the daily, 3-day cumulative, and 15- and 30-day antecedent rainfall values associated with landslides had been subjected to binary logistic regression using landslide as the dichotomous dependent variable. The logistic regression retained the daily, 3-day cumulative and 30-day antecedent rainfall values as significant predictors influencing slope failure. This model has been validated through receiver operating characteristic curve analysis using a set of samples which had not been used in the model building; an accuracy of 95.1 % has been obtained. Cross-validation of I–D-based thresholding and antecedent rainfall-based probability estimation with slope failure initiation shows 81.9 % conformity between the two in correctly predicting slope stability. Using the I–D-based threshold and the antecedent rainfall-based regression model, early warning can be generated for moderate to high landslide-susceptible areas (which can be delineated using spatial integration of preconditioning factors). Temporal predictions where both the methods converge indicate higher chances of slope failures for areas predisposed to instability due to unfavourable geo-environmental and topographic parameters and qualify for enhanced slope failure warning. This method can be verified for further rainfall seasons and can also be refined progressively with finer resolutions (spatial and temporal) of rainfall intensity and multiple rain gauge stations covering a larger spatial extent.