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81.
A Report on a Biotite-Calcic Hornblende Geothermometer 总被引:1,自引:0,他引:1
WU Chunming PAN Yusheng WANG Kaiyi ZHANG Jian Institute of Geology Chinese Academy of Geological Sciences Beijing Institute of Geology Geophysics Chinese Academy of Sciences Beijing Department of Earth Sciences The Graduate School Chinese Academy of Sciences Beijing 《《地质学报》英文版》2002,76(1):126-131
This paper presents a biotite-calcic hornblende geothermometer which was empirically calibrated based on the gamet-biotite geothermometer and the gamet-plagioclase-hornblende-quartz geobarometer, in the ranges of 560-800℃ (T) and 0.26-1.4 GPa (P) using the data of metadolerite, amphibolite, metagabbro, and metapelite collected from the literature. Biotite was treated as symmetric Fe-Mg-AlVI-Ti quaternary solid solution, and calcic hornblende was simplified as symmetric Fe-Mg binary solid solution. The resulting thermometer may rebuild the input garnet-biotite temperatures well within an uncertainty of ±50℃. Errors of ±0.2 GPa for input pressure, along with analytical errors of ?% for the relevant mineral compositions, may lead to a random error of ±16℃ for this thermometer, so that the thermometer is almost independent of pressure estimates. The thermometer may clearly discriminate different rocks of lower amphibolite, upper amphibolite and granulite facies on a high confidence level. It is assume 相似文献
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An electrical substation consists of a complex set of equipment items that are interconnected through conductor buses or cables. If the connections are not sufficiently flexible, significant dynamic interaction may occur between the connected equipment items during a seismic excitation. This interaction is believed to be responsible for some of the observed substation equipment damage in recent earthquakes. This paper investigates the interaction between two equipment items connected by a linear spring‐dashpot or spring‐dashpot‐mass element representing a conductor bus. It is found that the interaction between the two equipment items may significantly amplify the response of the higher‐frequency equipment item. The influences of various key parameters on the interaction effect are quantified. Means for reducing the adverse interaction effect are described. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
86.
针对纹理影像先估计出其马尔可夫随机场参数,后运用多元统计分析中模糊聚类分析的数学方法进行定量分类,从而为解决划分上的不确定性现象找出描述方法,获得客观的分类结果 相似文献
87.
David W. S. Wong 《The Professional geographer》1992,44(3):340-348
This paper demonstrates the utility of the iterative proportional fitting procedure (IPF) in generating disaggregated spatial data from aggregated data and evaluates the performance of the procedure. Estimates of individual level data created by IPF using data of equal-interval categories are reliable, but the performance of the estimation can be improved by increasing sample size. The improvement usually is enough to offset the increase in error created by other factors. If the two variables defining the cross-classification have a significant interaction effect and the number of categories in each variable is larger than two, then IPF is preferred over an independent model. 相似文献
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G. Christakos C. T. Miller D. Oliver 《Stochastic Environmental Research and Risk Assessment (SERRA)》1993,7(3):213-239
As is well known, a complete stochastic solution of the stochastic differential equation governing saturated groundwater flow leads to an infinite hierarchy of equations in terms of higher-order moments. Perturbation techniques are commonly used to close this hierarchy, using power-series expansions. These methods are applied by truncating the series after a finite number of terms, and products of random gradients of conductivity and head potential are neglected. Uncertainty regarding the number or terms required to yield a sufficiently accurate result is a significant drawback with the application of power series-based perturbation methods for such problems. Low-order series truncation may be incapable of representing fundamental characteristics of flow and can lead to physically unreasonable and inaccurate solutions of the stochastic flow equation. To support this argument, one-dimensional, steady-state, saturated groundwater flow is examined, for the case of a spatially distributed hydraulic conductivity field. An ordinary power-series perturbation method is used to approximate the mean head, using second-order statistics to characterize the conductivity field. Then an interactive perturbation approach is introduced, which yields improved results compared to low-order, power-series perturbation methods for situations where strong interactions exist between terms in such approximations. The interactive perturbation concept is further developed using Feynman-type diagrams and graph theory, which reduce the original stochastic flow problem to a closed set of equations for the mean and the covariance functions. Both theoretical and practical advantages of diagrammatic solutions are discussed; these include the study of bounded domains and large fluctuations. 相似文献
90.
Viet-Ha Nhu Khabat Khosravi James R. Cooper Mahshid Karimi Ozgur Kisi Binh Thai Pham 《水文科学杂志》2020,65(12):2116-2127
ABSTRACT The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone. 相似文献