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61.
The distributions of permeability and porosity are key factors that control airflow and gas phase transport in unsaturated formations. To understand the behavior of flow and transport in such formations, characterization procedure is a typical approach that has been widely applied to laboratories and fields. As is recognized by most investigations, this approach relies on accurate measurements, and more importantly, an adequate tool to interpret those measurements from experiments. This study presents a pneumatic inverse model that is capable to estimate the distributions of permeability (k) and porosity () with high resolution in heterogeneous unsaturated formations. Based on the concept of sequential successive linear estimator (SSLE), the developed model accounts for compressibility and density of air and estimates the geologic parameters using air pressure measurements from sequential cross-hole pneumatic pumping or injection tests. Four synthetic examples, including a one-dimensional well-posed, a horizontally two-dimensional, and two three-dimensional problems, are used to evaluate the developed model in estimating the distributions of permeability and porosity in unsaturated formations. Results of the numerical experiments are promising. The developed pneumatic inverse model can reconstruct the property (i.e., permeability and porosity) fields if the well-defined conditions are met. With a relatively small number of available measurements, the proposed model can accurately capture the patterns and the magnitudes of estimated properties for unsaturated formations. Results of two complex three-dimensional examples show that the proposed model can map the fracture connectivity using a small number of subsurface pressure measurements and estimate k and in shallow soil layers using spatial variations of barometric pressure. 相似文献
62.
Spatial interpolation of marine environment data using P-MSN 总被引:1,自引:0,他引:1
Bingbo Gao Maogui Hu Chengdong Xu Ziyue Chen Haimei Fan 《International journal of geographical information science》2020,34(3):577-603
ABSTRACTWhen a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for those environmental variables, an interpolated version of the mean of the surface with stratified non-homogeneity (MSN) method called point mean of the surface with stratified non-homogeneity (P-MSN) was derived. P-MSN distinguishes the spatial mean and variogram in different strata and borrows information from neighboring strata to improve the interpolation precision near the strata boundary. This paper also introduces the implementation of this method, and its performance is demonstrated in two case studies, one using ocean color remote sensing data, and the other using marine environment monitoring data. The predictions of P-MSN were compared with ordinary kriging, stratified kriging, kriging with an external drift, and empirical Bayesian kriging, the most frequently used methods that can handle some extent of spatial non-homogeneity. The results illustrated that for spatially stratified non-homogeneous environmental variables, P-MSN outperforms other methods by simultaneously improving interpolation precision and avoiding artificially abrupt changes along the strata boundaries. 相似文献
63.
星载原子钟在运行过程中会受到恶劣空间环境与设备老化等因素的影响,使得卫星钟差数据中经常存在异常值,其中AO(additive outlier)类异常值是钟差序列中常见的一类异常值.结合最大期望算法与自回归滑动平均(autoregressive moving average, ARMA)模型,提出一种AO类异常值探测算法... 相似文献
64.
J. A. Vargas-Guzmán T.-C. Jim Yeh 《Stochastic Environmental Research and Risk Assessment (SERRA)》1999,13(6):416-435
A sequential linear estimator is developed in this study to progressively incorporate new or different spatial data sets
into the estimation. It begins with a classical linear estimator (i.e., kriging or cokriging) to estimate means conditioned
to a given observed data set. When an additional data set becomes available, the sequential estimator improves the previous
estimate by using linearly weighted sums of differences between the new data set and previous estimates at sample locations.
Like the classical linear estimator, the weights used in the sequential linear estimator are derived from a system of equations
that contains covariances and cross-covariances between sample locations and the location where the estimate is to be made.
However, the covariances and cross-covariances are conditioned upon the previous data sets.
The sequential estimator is shown to produce the best, unbiased linear estimate, and to provide the same estimates and variances
as classic simple kriging or cokriging with the simultaneous use of the entire data set. However, by using data sets sequentially,
this new algorithm alleviates numerical difficulties associated with the classical kriging or cokriging techniques when a
large amount of data are used. It also provides a new way to incorporate additional information into a previous estimation. 相似文献
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“Krige”空间内插技术在地理学中的应用 总被引:24,自引:1,他引:24
称为“Krige”技术的内插稀疏观测资料的随机方法是Matheron(1970年)提出的,D.R.Krige首先将这一方法应用于找矿上,因而命名于“Krige”技术。本文首先定义和说明了空间协方差曲线,基于无偏估计和最优原理导出了“Krige”内插权重系数的代数方程组,最后给出实例说明该方法如何应用到地理学和水文学中。 相似文献
68.
69.
双k型岭估计及其在GPS快速定位中的应用 总被引:1,自引:0,他引:1
针对GPS快速定位中设计阵病态性的特点,文中对现有的有偏估计进行了改进,提出了一种新的有偏估计--双k型岭估计.由广义岭估计和普通岭估计出发,讨论并给出了双k型岭估计中两个岭参数的选择方法.最后给出了实测GPS动态定位算例,验证了新估计的稳定性和有效性. 相似文献
70.