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   677篇
  免费   34篇
  国内免费   23篇
测绘学   45篇
大气科学   40篇
地球物理   183篇
地质学   362篇
海洋学   25篇
天文学   37篇
综合类   8篇
自然地理   34篇
  2024年   1篇
  2023年   5篇
  2022年   22篇
  2021年   38篇
  2020年   37篇
  2019年   30篇
  2018年   67篇
  2017年   58篇
  2016年   91篇
  2015年   40篇
  2014年   65篇
  2013年   84篇
  2012年   48篇
  2011年   49篇
  2010年   28篇
  2009年   22篇
  2008年   12篇
  2007年   6篇
  2006年   8篇
  2005年   3篇
  2004年   4篇
  2003年   2篇
  2001年   1篇
  2000年   2篇
  1998年   3篇
  1997年   4篇
  1991年   1篇
  1975年   3篇
排序方式: 共有734条查询结果,搜索用时 15 毫秒
731.
In the present study, we investigate the effects of urbanization growth on river morphology in the downstream part of Talar River, east of Mazandaran Province, Iran. Morphological and morphometric parameters in 10 equal sub-reaches were defined along a 11.5 km reach of the Talar River after land cover maps were produced for 1955, 1968, 1994, 2005 and 2013. Land cover types changed extremely during the study period. Residential lands were found to have increased in area by about 1631%, while forest land and riparian vegetation decreased in by approximately 99.9 and 96.2%, respectively. The results of morphometric and morphological factors showed that average channel width (W) for all 11.5 km of the study river decreased by 84% during the study period, while the flow length increased by about 2.14%.  相似文献   
732.
Natural Resources Research - The weighted mean and the multiple regression techniques are two methods that are employed to estimate elemental background concentration of lithologies upstream of...  相似文献   
733.

Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled.

  相似文献   
734.

Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (Tcrit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of Tcrit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.

  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