首页 | 本学科首页   官方微博 | 高级检索  
文章检索
  按 检索   检索词:      
出版年份:   被引次数:   他引次数: 提示:输入*表示无穷大
  收费全文   9878篇
  免费   2045篇
  国内免费   2616篇
测绘学   700篇
大气科学   2132篇
地球物理   2766篇
地质学   4983篇
海洋学   1460篇
天文学   336篇
综合类   955篇
自然地理   1207篇
  2024年   28篇
  2023年   139篇
  2022年   406篇
  2021年   464篇
  2020年   362篇
  2019年   503篇
  2018年   552篇
  2017年   500篇
  2016年   551篇
  2015年   496篇
  2014年   613篇
  2013年   625篇
  2012年   698篇
  2011年   679篇
  2010年   723篇
  2009年   680篇
  2008年   658篇
  2007年   622篇
  2006年   569篇
  2005年   475篇
  2004年   415篇
  2003年   394篇
  2002年   319篇
  2001年   325篇
  2000年   348篇
  1999年   328篇
  1998年   289篇
  1997年   287篇
  1996年   249篇
  1995年   233篇
  1994年   209篇
  1993年   193篇
  1992年   131篇
  1991年   87篇
  1990年   74篇
  1989年   73篇
  1988年   56篇
  1987年   53篇
  1986年   35篇
  1985年   19篇
  1984年   15篇
  1983年   9篇
  1982年   11篇
  1981年   6篇
  1980年   8篇
  1979年   3篇
  1978年   3篇
  1977年   4篇
  1958年   6篇
  1954年   4篇
排序方式: 共有10000条查询结果,搜索用时 15 毫秒
1.
This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.  相似文献   
2.
ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   
3.
The auriferous veins at Jinniushan occurs within the Jinniushan faulted zone in the Kunyushan Granite. Optical observation reveals that gold ore body formed during the main stage of hydrothermal activity. Detailed geothermometric studies of fluid inclusions from the veins show that the forming temperature ranges between 130℃ and 370℃ and the salinity is from 4.01 to 15.21 wvt percent NaCl. The ore-forming fluid is featured by low to moderate salinity, and low to moderate temperature. According to investigations of the values of vapor/liquid and temperatures of the ore-forming fluids, we propose that the boiling fluid inclusions exist in the main mineralization stages. Fluid boiling is suggested as a mechanism for the precipitation of gold from the hydrothermal fluid in the Jinniushan gold deposit.  相似文献   
4.
利用钻孔测井资料并运用地层倾角测量信息分析法,给出了江汉盆地地应力最大水平主压应力方向为NE60~65°  相似文献   
5.
DEVELOPMENTSTRATEGIESOFWATERANDLANDRESOURCESINTHEHEXIREGION,CHINA肖洪浪,高前兆,李福兴DEVELOPMENTSTRATEGIESOFWATERANDLANDRESOURCESINTHE...  相似文献   
6.
通过对辽北法库地区构造岩的观察和研究,确定了本区存在一大型推覆韧性剪切带。根据同构造侵入岩的同位素年龄,确定该韧性剪切带形成时期为海西-印支期,这与天山-兴安褶皱系最终形成时间相一致  相似文献   
7.
An improved Solar Radio Spectrometer working at 1.10-2.06 GHz with much improved spectral and temporal resolution, has been accomplished by the National Astronomical Observatories and Hebei Semiconductor Research Institute, based on an old spectrometer at 1-2 GHz. The new spectrometer has a spectral resolution of 4 MHz and a temporal resolution of 5ms, with an instantaneous detectable range from 0.02 to 10 times of the quiet Sun flux. It can measure both left and right circular polarization with an accuracy of 10% in degree of polarization. Some results of preliminary observations that could not be recorded by the old spectrometer at 1-2 GHz are presented.  相似文献   
8.
宁波盆地地下揭示的一套包含暗色膏硝质泥岩、泥质白云岩在内的紫红、灰紫色泥岩、棕褐色砂砾岩、细砂岩和玻屑凝灰岩的地层,均称方岩组,内含膏盐并具油色显示。对其时代有早、晚白垩世和早第三纪之认识,笔者从70~90年代地质工作中所获化石分析认为,虽然宁波盆地这一层位含化石不丰,但从分布及数量上比较,相对占优势的应该是孢粉和植物化石,其时代意见也较为一致,指示为早白垩世。  相似文献   
9.
Boli basin, between Yishu fracture belt and Dunmi fracture belt, is the biggest Mesozoic coal basin in the east of Heilongjiang Province. Now it is a fault - fold remnant basin. The basin' s shape is generally consistent with the whole distribution of the cover folds, an arc protruding southwards. The basement of the basin can be divided into three fault blocks or structural units. The formation and evoluation of the basin in Mesozoic was determined by the basement fault blocks' displacement features rusulted from by the movement of the edge faults and the main basement faults.  相似文献   
10.
The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m3/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m2·d)/(m3/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m2·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.  相似文献   
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号