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181.
试论胶东金成矿区成矿物质条件 总被引:9,自引:1,他引:9
对胶东群、粉子山群和蓬莱群的岩石学、地球化学等特征研究认为,胶东群是金的主要矿源层;粉子山群和煌斑岩脉也提供了部分金的来源。花岗质岩浆活动促使成矿物质转移。 相似文献
182.
叠覆式三角洲——一种特殊的浅水三角洲 总被引:2,自引:0,他引:2
不同于常规三角洲以分流河道体系所形成的分流河道、河口坝、席状砂等微相为三角洲朵体的基本单元,叠覆式三角洲以内部结构简单的朵体为基本构成单元,朵体相互叠置,形成复合叠合体,进而构成三角洲骨架。单个朵体由河道扫描或扩展而成,复合朵体则是由单朵体侧向迁移或前(退)积而成。三角洲因大量朵体叠置而形成厚层状、内部结构复杂的复合砂体。不同朵体形成于不同时期,因而不存在统一的分流体系,单一沉积体具有层状特征,但不同期朵体受可容空间和地貌控制,呈三维叠置,而非简单的层状叠加,从而使得三角洲内部呈现出拼合式、立体式特点。单朵体是结构的基本单元,发育范围有限,与相邻朵体发育于不同时间单元,因而只能在复合体约束下小范围追踪。单一朵体接触关系及接触界面的渗流能力决定了油气富集和注水开发响应特征。朵体迁移、叠置造成大面积、巨厚的砂层可形成大型油气藏,而同时朵体间泥岩的不均匀分布也造就了砂体局部不连通或朵体间连通性变化,为岩性油气藏形成创造了条件,并且影响了注水开发中的注采对应性,进而影响水驱采油效果。 相似文献
183.
184.
Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks 总被引:1,自引:0,他引:1
Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network (PBNN) nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter α k , which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion. 相似文献
185.
Elastic reverse-time migration (RTM) can reflect the underground elastic information more comprehensively than single-component Pwave migration. One of the most important requirements of elastic RTM is to solve wave equations. The imaging accuracy and efficiency of RTM depends heavily on the algorithms used for solving wave equations. In this paper, we propose an efficient staggered-grid finite-difference (SFD) scheme based on a sampling approximation method with adaptive variable difference operator lengths to implement elastic prestack RTM. Numerical dispersion analysis and wavefield extrapolation results show that the sampling approximation SFD scheme has greater accuracy than the conventional Taylor-series expansion SFD scheme. We also test the elastic RTM algorithm on theoretical models and a field data set, respectively. Experiments presented demonstrate that elastic RTM using the proposed SFD scheme can generate better images than that using the Taylor-series expansion SFD scheme, particularly for PS images. FurH. thermore, the application of adaptive variable difference operator lengths can effectively improve the computational efficiency of elastic RTM. 相似文献
186.
Science China Earth Sciences - In order to better understand the role of post-collisional mafic magmatism at convergent plate boundaries in revealing the earth’s evolution, this paper has... 相似文献
187.
188.
海拉尔盆地贝尔凹陷白垩系南屯组和铜体庙组凝灰质砂岩、凝灰质泥岩、沉凝灰岩和凝灰岩储层是该区主要产层。油气储集空间以次生孔隙为主。大量碱性矿物组合如柯绿泥石、钠板石、碳钠铝石、方沸石、绿磷石、铈褐帘石、铁绿泥石等的存在,证明发生了大规模碱性溶解和交代作用。晶屑、玻屑、长石和粘土等填隙物均发生不同程度的溶解现象。研究表明,次生孔隙是凝灰质储层大规模碱交代作用的结果,预示盆地深部存在油气储集空间并富集油气。 相似文献
189.
The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling 总被引:8,自引:3,他引:8
Chong Xu Xiwei Xu Yuan Hsi Lee Xibin Tan Guihua Yu Fuchu Dai 《Environmental Earth Sciences》2012,66(6):1603-1616
The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between
96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system
(GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total
of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake,
and verified by selected field checking before a final landslide-inventory map of the study area could be established using
GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628
landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated
controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from
main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from
drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were
calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to
obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement
between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate
is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate,
low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction
work. 相似文献
190.