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201.
Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario 总被引:4,自引:0,他引:4
A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO
2
are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO
2
), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols
c
i
Scalar factor that minimizes the discrepancy between andU
i
-
D
Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients
-
d
Distance for which transition matrixU is estimated
-
d
ij
Distance between observed valuesi andj
-
E
Expected value
-
E
i
Row vector of residuals in the standardized model
-
F(d
ij)
Quadratic function of distanced
ij F(d
ij)=a+bd
ij+cd
ij
2
-
L
Diagonal matrix of the eigenvalues ofU
-
i
Eigenvalue of the matrixU;ith diagonal element ofL
-
N
Number of observations
-
p
Number of variables
-
Q
Total predictive power ofU
-
R
Correlation matrix of the variables
-
R
0j
Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m)
-
R
1j
Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood
-
R
m
2
Multiple correlation coefficient squared for themth variable
-
S
i
Column vectori of the signal values
-
s
k
2
Residual variance for variablek
-
T
i
Amplitude vector corresponding toV
i;ith row ofT=V
–1
-
T
Total variation in the system
-
U
Nonsymmetric transition matrix formed by post-multiplyingR
01
–1
byR
ij
-
U
i
Componenti of the matrixU, corresponding to theith eigenvectorV
i;U
i=
iViTi
-
U*
i
ComponentU
i multiplied byc
i
-
U
ij
Sum of componentsU
i+U
j
-
V
i
Eigenvector of the matrixU;ith column ofV withUV=VL
-
w
Weighting factor; equal to the ratio of two eigenvalues
-
X
i
Random variable at pointi
-
x
i
Value of random variable at pointi
-
y
i
Residual ofx
i
-
Z
i
Row vectori for the standardized variables
-
z
i
Standardized value of variable 相似文献
202.
When do we need a trend model in kriging? 总被引:1,自引:0,他引:1
Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option. 相似文献
203.
在急倾斜构造部位大力使用硬合金回转钻进时,常常会产生弯曲度很大的走向偏斜情况。这是因为不同岩层的阻力差异使钻头受到一种平行于岩层走向的偏斜力,迫使钻头向侧方研磨井壁;在不同井径变换部位所产生的偏斜效果是不同的。这样造成钻孔沿地层走向发生有规律的定向偏斜。 相似文献
204.
介绍了一种雾的客观预报方法,并研制了一个与STYS接口的雾的客观预报自动化业务系统,对雾有一定的预报能力。 相似文献
205.
根据对殷庄矿地质、水文地质、开采条件与矿井涌水之间关系的研究,提出了煤矿开采水文地质条件预测分区的方法——多源信息方法。该方法借助于地理信息系统技术实现了矿井涌水因素间的有机处理。经过将模型与实际情况反复拟合,最终得出了分区预测模型。 相似文献
206.
The available data on known reserves of extracted resources have not previously been tabulated and graphed. In this article we compile the reserves data in one place for the first time. This serves to (1) show the trends in the reserves series, (2) make the series available to others, and (3) place the reserves data in the context of price data for which longer series have been available, and with which they can now be seen to be consistent in both showing increasing availability rather than increasing scarcity. 相似文献
207.
Kyungcho Bae Recent PhD graduate in Mineral Economics DeVerle Harris 《Natural Resources Research》1995,4(4):325-339
Monthly consumption forecasts for U.S. oil, natural gas, and coal are made using state space and multiple regression applied to the same data. These forecasts are compared with actual consumption for a test period. The forecasts made using state space are preferred to those made using multiple regression models for both expost and exante cases. The state space forecasts track data cycles better than do the regression forecasts. Average absolute forecast errors are less for the state space models than they are for the multiple regression models. 相似文献
208.
通过横流太平洋往返两个航次随船调查,研究了采用气象导航对减少燃油消耗和保证船舶安全、班期等,不但有定的经济效益,而且是一种非常有效的航运管理方法。 相似文献
209.
塔里木盆地绿洲形成与演变 总被引:62,自引:3,他引:62
塔里木盆地的绿洲按形成历史可划分为古绿洲、旧绿洲和新绿洲,它们分别代表绿洲发展演变的三个阶段。本文对绿洲未来演变趋势作了预测,探讨了盆地古绿洲衰亡原因、认为除了风沙、盐碱及河流改道等自然原因外,人类活动引起的地表水资源时空分配发生变化是主要原因。 相似文献
210.
本文把1958—1984年天山乌鲁木齐河源1号冰川的融水径流总量的时间序列X(t),分解为趋势项L(t)、周期项P(t)、平稳项S(t)、随机项ε(t),使该时间序列表示为X(t)=L(t)+P(t)+S(t)+ε(t)。采用非线性回归提取L(t),用谱分析和Fourier级数提取P(t),余差用自回归方程建模,用上述项的叠加作出预报,按相关指数公式计算R=0.90,效果良好。 相似文献