Numerical Analysis of Space–Time Finite Element Formulations for Miscible Displacements

A finite element formulation is proposed to approximate a nonlinear system of partial differential equations, composed by an elliptic subsystem for the pressure–velocity and a transport equation (convection–diffusion) for the concentration, which models the incompressible miscible displacement of one fluid by another in a rigid porous media. The pressure is approximated by the classical Galerkin method and the velocity is calculated by a post-processing technique. Then, the concentration is obtained by a Galerkin/least-squares space–time (GLS/ST) finite element method. A numerical analysis is developed for the concentration approximation. Then, stability, convergence and numerical results are presented confirming the a priori error estimates.     

Simulation of artificial random field considering the temporal-spatial variation

Introduction For the seismic design of special structures such as nuclear power station, marine platform, long-span bridge and dam, generally the time-history response analysis of the structure under seismic excitation is imperative, which was coded in most seismic design codes. The earthquake records suitable for the seismic situation and site condition are necessary to be used as the seismic input in the dynamic analysis of structures. As a result of the limited observational condition of st…     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.     

高层钢框架-支撑结构二阶非线性随机地震响应分析

针对常用的高层钢框架－支撑结构，考虑材料非线性和几何非线性，建立了框架部分、支撑部分的二阶动力分析模型。用等效线性方法对结构进行非线性随机地震响应分析，结合工程算例，论述不同场地土、不同层数、不同支撑情况下，二阶效应对结构地震响应统计量的影响。     

Precise revision of the garnet-muscovite geothermometer

The garnet-muscovite geothermometer was refined through empirical calibration by using natural rocks metamorphosed under the physical conditions of 238—1306 MPa and 490—700℃. Input temperatures and pressures were determined through simultaneously applying the garnet-biotite geothermometer and the garnet-biotite-plagioclase-quartz barometer, assuming that all FeO in muscovite and garnet be ferrous. Garnet was treated as the asymmetric quaternary solid solution, and muscovite as the symmetric binary solid solution. Input muscovite compositions include Fe atoms between 0.03—0.19 and Mg atoms between 0.04—0.16 on the basis of 11 oxygen atoms, and input garnet compositions include spessartine fractions between 0.01—0.289, grossular fractions between 0.028—0.273, and the Fe/Mg ratio between 3.387-18.986. The resulting garnet-muscovite geothermometer reproduces temperatures within (50℃ compared with the garnet-biotite thermometer. Total random error of ±37℃ of the new thermometer may stem from the pressure uncertainty of ±200 MPa, and uncertainties of ±5% of Fe and Mg components in muscovite, and ±5% of Fe, Mg, Mn and Ca components in garnet, altogether. When there exist 10%, 20%, 30%, 40% and 50% Fe3+ in muscovite, respectively, the computed garnet-muscovite temperatures will be 1—6℃, 2—12℃, 3—16℃, 5—24℃ and 7—29℃, respectively, lower than those obtained when assuming that all FeO be ferrous. The new garnet-muscovite geothermometer can efficiently reflect temperature change of typical prograde sequences and contact aureole rocks, and may be applied to low- to high-grade and low- to high-pressure metamorphic rocks.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

GPS在地面沉降监测中的应用探讨

利用GPS进行地面沉降监测能够解决传统测量方法工作量大、周期长的问题。该文结合实例，论述了GPS在地面沉降监测中的应用，分析了GPS测量方法的优点和误差，提出了GPS测量地面沉降的适用条件。     

机械卡盘设计中的计算误差分析与应用

通过对普通机构二维力因素影响的分析，利用修正公式计算求值，以修正单因素影响最大值计算所产生的误差。利用这一计算方法，对CD—3型钻机卡盘进行了计算，得到了正确的设计结果，说明用修正公式对多因素影响采用单因素影响最大值计算进行修正是可行的。