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141.
解算了2001~2005年新疆周边发生的3次大震产生的库仑应力变化,同时结合其后3年内库仑应力正负区域5级以上地震活动特征,并引用GPS资料综合分析了这组周边大震活动对新疆境内地震趋势的影响。研究结果表明,新疆周边这组大震活动一定程度上减缓了新疆境内中强地震的孕震过程。 相似文献
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143.
The optical properties and spatial distribution of chromophoric dissolved organic matter (CDOM) in Meiliang Bay of Lake Taihu were evaluated and compared to the results in literature. Concentrations of dissolved organic carbon (DOC) ranged from 8.75 to 20.19 mg L?1 with an average of (13.10 ± 3.51) mg L?1. CDOM absorption coefficients a(λ) at 280 nm, 355 nm, and 440 nm were in the range 11.28...33.46 m?1 (average (20.95 ± 5.52) m?1), 2.42...7.90 m?1 (average (4.92 ± 1.29) m?1), and 0.65...2.44 m?1 (average (1.46 ± 0.44) m?1), respectively. In general, CDOM absorption coefficient and DOC concentration were found to decrease away from the river inflow to Meiliang Bay towards the lake center. The values of the DOC‐specific absorption coefficients a*(λ), given as absorption coefficient related to mass concentration of organic carbon (C) ranged from 0.28 to 0.47 L mg?1 m?1 at 355 nm. The determination coefficients between CDOM absorption and DOC concentration decreased with the increase of wavelength from 280 to 550 nm. The linear regression relationship between CDOM absorption at 280 nm and DOC concentration was following: a(280 nm) = 1.507 L mg?1 m?1 · DOC + 1.215 m?1. The spectral slope S values were dependent on the wavelength range used in the regression. The estimated S values decreased with increasing wavelength range used. A significant negative linear relationship was found between CDOM absorption coefficients, DOC‐specific absorption coefficients and estimated S values especially in longer wavelength range. The linear regression relationship between DOC‐specific absorption coefficients at 440 nm and estimated S values during the wavelength range from 280 to 500 nm was following: a*(440 nm) = (–0.021 μm · S + 0.424) L mg?1 m?1. 相似文献
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145.
Dieter Kley 《Journal of Atmospheric Chemistry》1984,2(2):203-210
The absorption cross-sections of water vapor and oxygen were measured, using a low-pressure radio frequency discharge through traces of hydrogen in argon as a light source for Ly() radiation. The cross-sections are H2O = 1.59 × 10–17 cm2 and O2 = 1.13 × 10–20 + 1.72 × 10–23 for water and oxygen, respectively, where P is the oxygen pressure in units of Torr. Ly() lamps, such as used for this work, are important light sources for photochemical laboratory work and find applications for trace-gas detection in the atmosphere. For the latter application, accurate cross-sections of water vapor and oxygen are needed. 相似文献
146.
In this study, we compare three commonly used methods for hyperspectral image classification, namely Support Vector Machines (SVMs), Gaussian Processes (GPs) and the Spectral Angle Mapper (SAM). We assess their performance in combination with different kernels (i.e. which use distance-based and angle-based metrics). The assessment is done in two experiments, under ideal conditions in the laboratory and, separately, in the field (an operational open pit mine) using natural light. For both experiments independent training and test sets are used. Results show that GPs generally outperform the SVMs, irrespective of the kernel used. Furthermore, angle-based methods, including the Spectral Angle Mapper, outperform GPs and SVMs when using distance-based (i.e. stationary) kernels in the field experiment. A new GP method using an angle-based (i.e. a non-stationary) kernel – the Observation Angle Dependent (OAD) covariance function – outperforms SAM and SVMs in both experiments using only a small number of training spectra. These findings show that distance-based kernels are more affected by changes in illumination between the training and test set than are angular-based methods/kernels. Taken together, this study shows that independent training data can be used for classification of hyperspectral data in the field such as in open pit mines, by using Bayesian machine-learning methods and non-stationary kernels such as GPs and the OAD kernel. This provides a necessary component for automated classifications, such as autonomous mining where many images have to be classified without user interaction. 相似文献
147.
电化学氢化物发生法(EcHG)是原子光谱仪发展的一种实用气态进样技术。该技术通过采用电化学发生池内的电极反应代替传统化学还原的方法来生成氢化物和汞蒸气。与传统的化学法硼氢化钾(钠)-酸氢化物发生体系相比,EcHG技术仅需要支持电解质,氢化物(汞蒸气)在阴极室内发生后直接导入原子光谱仪的原子化器,在降低分析成本和溶液配制时间的同时,分析过程引入的空白值也大大降低,更加绿色环保。近年来,EcHG原子光谱分析已经从单一的元素总量测定发展到元素形态分析,从微量元素分析发展到痕量超痕量元素分析,发生元素涵盖了砷、硒、铅、镉、锡、锑、锗和汞,应用范围涉及食品、环境、烟草、饲料等实际样品。EcHG技术应用于原子荧光光谱分析,特征元素检出限能够达到0.1 μg/L级(汞为ng/L级);应用于原子吸收光谱与等离子体发射光谱分析,适用元素检出限能够达到μg/L级,相对标准偏差均小于10%,回收率在90%~110%之间。EcHG技术相关的机理研究也已经起步,这为该技术在原子光谱分析领域的应用提供了理论基础。但是,EcHG技术的分析范围目前仅限于部分元素的无机态,对元素的有机形态分析是本领域发展的难点之一。本文提出,关于电化学氢化物发生的机理研究、电化学流通池结构的优化、形态分析范围的拓展等将成为该技术的重要发展方向。 相似文献