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   337篇
  免费   13篇
  国内免费   2篇
测绘学   10篇
大气科学   29篇
地球物理   108篇
地质学   129篇
海洋学   17篇
天文学   26篇
自然地理   33篇
  2022年   3篇
  2021年   4篇
  2020年   14篇
  2019年   9篇
  2018年   4篇
  2017年   6篇
  2016年   15篇
  2015年   14篇
  2014年   7篇
  2013年   19篇
  2012年   16篇
  2011年   17篇
  2010年   21篇
  2009年   21篇
  2008年   16篇
  2007年   15篇
  2006年   12篇
  2005年   8篇
  2004年   11篇
  2003年   3篇
  2002年   5篇
  2001年   9篇
  2000年   12篇
  1999年   10篇
  1998年   4篇
  1997年   4篇
  1996年   5篇
  1995年   4篇
  1994年   3篇
  1993年   2篇
  1991年   2篇
  1990年   6篇
  1989年   6篇
  1988年   4篇
  1987年   3篇
  1986年   4篇
  1985年   4篇
  1984年   6篇
  1983年   2篇
  1982年   3篇
  1979年   1篇
  1977年   2篇
  1976年   3篇
  1975年   1篇
  1974年   3篇
  1973年   2篇
  1972年   1篇
  1971年   1篇
  1969年   1篇
  1963年   1篇
排序方式: 共有352条查询结果,搜索用时 31 毫秒
351.
The Almahata Sitta (AhS) meteorite consists of disaggregated clasts from the impact of the polymict asteroid 2008 TC3, including ureilitic (70%–80%) and diverse non-ureilitic materials. We determined the 40Ar/39Ar release patterns for 16 AhS samples (3–1500 μg) taken from three chondritic clasts, AhS 100 (L4), AhS 25 (H5), and MS-D (EL6), as well as a clast of ureilitic trachyandesite MS-MU-011, also known as ALM-A, which is probably a sample of the crust of the ureilite parent body (UPB). Based on our analyses, best estimates of the 40Ar/39Ar ages (Ma) of the chondritic clasts are 4535 ± 10 (L4), 4537–4555 with a younger age preferred (H5), and 4513 ± 17 (EL6). The ages for the L4 and the H5 clasts are older than the most published 40Ar/39Ar ages for L4 and H5 meteorites, respectively. The age for the EL6 clast is typical of older EL6 chondrites. These ages indicate times of argon closure ranging up to 50 Ma after the main constituents of the host breccia, that is, the ureilitic components of AhS, reached the >800°C blocking temperatures of pyroxene and olivine thermometers. We suggest that these ages record the times at which the clasts cooled to the Ar closure temperatures on their respective parent bodies. This interpretation is consistent with the recent proposal that the majority of xenolithic materials in polymict ureilites were implanted into regolith 40–60 Ma after calcium–aluminum-rich inclusion and is consistent with the interpretation that 2008 TC3 was a polymict ureilite. With allowance for its 10-Ma uncertainty, the 4549-Ma 40Ar/39Ar age of ALM-A is consistent with closure within a few Ma of the time recorded by its Pb/Pb age either on the UPB or as part of a rapidly cooling fragment. Plots of age versus cumulative 39Ar release for 10 of 15 samples with ≥5 heating steps indicate minor losses of 40Ar over the last 4.5 Ga. The other five such samples lost some 40Ar at estimated times no earlier than 3800–4500 Ma bp . Clustering of ages in the low-temperature data for these five samples suggests that an impact caused localized heating of the AhS progenitor ~2.7 Ga ago. In agreement with the published work, 10 estimates of cosmic-ray exposure ages based on 38Ar concentrations average 17 ± 5 Ma but may include some early irradiation.  相似文献   
352.

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