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Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account
for spatial dependence in the observed data. However, the assumption that the spatial dependence is constant throughout the
region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of,
e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from
spatial statistics for non-stationary covariance structures, and demonstrate its advantage for dealing with non-stationarity
in geodetic data. We then compared stationary and non- stationary covariance functions in 2D LSC to the empirical example
of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary.
The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation
against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve
upon standard (stationary) LSC. 相似文献
266.
This study presents a probabilistic neural network (PNN) technique for predicting the stability number of armor blocks of breakwaters. The PNN is prepared using the experimental data of Van der Meer. The predicted stability numbers of the PNN are compared with those of previous studies, i.e. by an empirical formula and a previous neural network model. The agreement index between the measured and predicted stability numbers by PNN are better than those by the previous studies. The PNN offers a way to interpret the network's structure in the form of a probability density function and it is easy to implement. Therefore, it can be an effective tool for designers of rubble mound breakwaters. 相似文献
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Statistical analysis and forecasts of long-term sandbank evolution at Great Yarmouth, UK 总被引:4,自引:1,他引:3
Dominic E. Reeve Jos M. Horrillo-Caraballo Vanesa Magar 《Estuarine, Coastal and Shelf Science》2008,79(3):387-399
A data-driven model has been developed to analyse the long-term evolution of a sandbank system and to make ensemble predictions in a period of 8 years. The method uses a combination of empirical orthogonal function (EOF) analysis, (to define spatial and temporal patterns of variability), jack-knife resampling, (to generate an ensemble of EOFs), a causal auto-regression technique, (to extrapolate the temporal eigenfunctions), and straightforward statistical analysis of the resulting ensemble of predictions to determine a ‘forecast’ and associated uncertainty. The methodology has been applied to a very demanding site which includes a curved shoreline and a group of mobile nearshore sandbanks. The site is on the eastern coast of the UK and includes the Great Yarmouth sandbanks and neighbouring shoreline. A sequence of 33 high quality historical survey charts reaching back to 1848 have been used to analyse the patterns and to predict morphological evolution of the sandbank system. The forecasts demonstrate an improved skill relative to an assumption of persistence, but suffer in locations where there are propagating features in the morphology that are not well-described by EOFs. 相似文献
269.
Robert E. Reed David A. Dickey JoAnn M. Burkholder Carol A. Kinder Cavell Brownie 《Estuarine, Coastal and Shelf Science》2008
Water level time series records from the Neuse and Pamlico River Estuaries were statistically compared to local and distant wind field data, water level records within the Pamlico Sound and also coastal ocean sites to determine the relative contribution of each time series to water levels in the Neuse and Pamlico Estuaries. The objectives of this study were to examine these time series data using various statistical methods (i.e. autoregressive, empirical orthogonal function analysis (EOF), exploratory data analysis (EDA)) to determine short- and long-time-scale variability, and to develop predictive statistical models that can be used to estimate past water level fluctuations in both the Neuse Estuary (NE) and Pamlico Estuary (PE). Short- and long-time-scale similarities were observed in all time series of estuarine, Pamlico Sound and subtidal coastal ocean water level and wind component data, due to events (nor'easters, fronts and tropical systems) and seasonality. Empirical orthogonal function analyses revealed a strong coastal ocean and wind field contribution to water level in the NE and PE. Approximately 95% of the variation was captured in the first two EOF components for water level data from the NE, sound and coastal ocean, and 70% for the PE, sound and coastal ocean. Spectral density plots revealed strong diurnal signals in both wind and water level data, and a strong cross correlation and coherency between the NE water level and the North/South wind component. There was good agreement between data and predictions using autoregressive statistical models for the NE (R2 = 0.92) and PE (R2 = 0.76). These methods also revealed significant autoregressive lags for the NE (days 1 and 3) and for the PE (days 1, 2 and 3). Significant departures from predictions are attributed to local meteorological and hydrological events. The autoregressive techniques showed significant predictive improvement over ordinary least squares methods. The results are considered within the context of providing long time-scale hindcast data for the two estuaries, and the importance of these data for multidisciplinary researchers and managers. 相似文献
270.
无限水深聚焦波完全非线性数值模拟 总被引:1,自引:1,他引:0
基于势流理论提出一种新的高阶边界元方法对无限水深的聚焦波浪进行完全非线性数值模拟.自由水面满足完全非线性边界条件,模拟波浪的非线性效果可以达到更高阶.利用镜像原理,建立一种全新的格林函数应用到无限水深的数值波浪水槽中,以致于两无限深水槽侧壁的积分可以被排除.为了产生相应的入射波和吸收出流波浪,一个由点源组成的造波装置被布置于计算域内,同时人工阻尼层被用来吸引出流波浪,由波浪聚焦的方法得到极限波浪.通过开展线性和完全非线性聚焦波浪的数值实验及与理论解对比,验证本数值模型可以用来模拟无限深水域的极限波浪,且在出流边界没有反射. 相似文献