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941.
吴道荣 《中国地质灾害与防治学报》2007,18(3):108-111
以国内广泛使用的传递系数法的平衡方程为基础,运用"国际结构安全度联合委员会(JCSS)"推荐的验算点法建立边坡工程可靠度分析模型,分别分析了某边坡基于随机变量模型和随机场模型时的边坡稳定性可靠度和破坏概率。计算结果表明,用随机变量模型计算出的破坏概率明显偏大,采用随机场模型比较符合实际情况。本例采用随机场模型计算时,方差折减系数可取0.5。 相似文献
942.
943.
基于动力可靠度的隔震结构参数模糊优化 总被引:1,自引:0,他引:1
分别选取Bouc—Wen和退化Bouc—Wen模型描述隔震层及上部结构滞变回复力,采用虚拟激励法进行隔震结构随机响应分析。用各层最大层间位移响应和累计疲劳损伤指数建立双参数的功能状态方程,用一次二阶矩理论计算隔震结构失效概率。选用一个隔震框架作为数值算例,探讨了低频过滤器、隔震阻尼比和隔震刚度对隔震结构各子系统条件失效概率的影响。建立了隔震结构参数多目标优化模型,用失效概率确定单因素评判的隶属度,并采用最大隶属度法对隔震参数进行模糊优化。 相似文献
944.
Weidong Li 《Mathematical Geology》2007,39(3):321-335
Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating
categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces
a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation
(or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional
probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general
solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’
theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional
simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models
are nonlinear. 相似文献
945.
946.
Places of formation have been derived for a sample of 23 Cepheids with well-determined atmospheric abundances in an attempt
to study the chemical inhomogeneities in the local interstellar medium. The abundance data available for the sample Cepheids
is compiled and critically reviewed to adopt the most reliable esimates. We find that the most conspicuous irregularity in
the metallicity is exhibited by stars that are born in the local arm or in the interarm region. We propose a scenario to explain
these local variations in terms of supernova-induced star formation in interstellar gas enriched by massive stars formed in
the density wave. 相似文献
947.
Robert I. Odom 《Geophysical Journal International》1986,86(2):425-453
Summary. A coupled mode theory is used to examine surface wave propagation in a laterally inhomogeneous acoustic waveguide. The theory is developed from the equations of motion for the pressure and velocity fields. The presence of lateral inhomogeneities in the form of varying layer thickness causes coupling among the discrete modes of the waveguide and radiation to the continuum. Expressions for the coupling coefficients among all mode types including coupling to the continuum spectrum are derived. The coupling coefficients are proportional to the horizontal derivative of the function describing the interface between layers of constant material properties but varying thickness. The coupled mode equations are solved in approximation for the case of a sinusoidal boundary and a sloping boundary. The results for radiation losses due to interaction with the irregular boundary of the waveguide are presented in analytical form, which clearly show the primary physical effects on the wavefield of the interaction. The far field amplitude of the scattered modes, excited by the interaction of some incident signal with a weak boundary irregularity, is modulated by the spatial Fourier transform of the irregularity. 相似文献
948.
949.
950.
Andrew R. Solow 《Mathematical Geology》1993,25(1):53-57
A common problem in geostatistics is to determine whether or not the value of a random field at an unsampled location exceeds a specified threshold using observed values of the random field at sampled locations. Under the indicator approach, the only information used to classify the unobserved value is whether or not the observed values exceed the threshold. This note shows that the loss of information from applying the indicator approach may be modest in the case where the underlying random field is Gaussian. 相似文献