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Traditionally the Cooper–Jacob equation is used to determine the transmissivity and the storage coefficient for an aquifer using pump test results. This model, however, is a simplified version of the actual subsurface and does not allow for analysis of the uncertainty that comes from a lack of knowledge about the heterogeneity of the environment under investigation. In this paper, a modified fuzzy least-squares regression (MFLSR) method is developed that uses imprecise pump test data to obtain fuzzy intercept and slope values which are then used in the Cooper–Jacob method. Fuzzy membership functions for the transmissivity and the storage coefficient are then calculated using the extension principle. The supports of the fuzzy membership functions incorporate the transmissivity and storage coefficient values that would be obtained using ordinary least-squares regression and the Cooper–Jacob method. The MFLSR coupled with the Cooper–Jacob method allows the analyst to ascertain the uncertainty that is inherent in the estimated parameters obtained using the simplified Cooper–Jacob method and data that are uncertain due to lack of knowledge regarding the heterogeneity of the aquifer. 相似文献
145.
As for the satellite remote sensing data obtained by the visible and infrared bands inversion, the clouds coverage in the sky over the ocean often results in missing data of inversion products on a large scale, and thin clouds di?cult to be detected would cause the data of the inversion products to be abnormal. Alvera et al.(2005) proposed a method for the reconstruction of missing data based on an Empirical Orthogonal Functions (EOF) decomposition, but his method couldn’t process these images presenting ex... 相似文献
146.
Anton Egorov Stanislav Glubokovskikh Andrej Bóna Roman Pevzner Boris Gurevich Mikhail Tokarev 《Geophysical Prospecting》2018,66(1):3-12
Most seismic processing algorithms generally consider the sea surface as a flat reflector. However, acquisition of marine seismic data often takes place in weather conditions where this approximation is inaccurate. The distortion in the seismic wavelet introduced by the rough sea may influence (for example) deghosting results, as deghosting operators are typically recursive and sensitive to the changes in the seismic signal. In this paper, we study the effect of sea surface roughness on conventional (5–160 Hz) and ultra‐high‐resolution (200–3500 Hz) single‐component towed‐streamer data. To this end, we numerically simulate reflections from a rough sea surface using the Kirchhoff approximation. Our modelling demonstrates that for conventional seismic frequency band sea roughness can distort results of standard one‐dimensional and two‐dimensional deterministic deghosting. To mitigate this effect, we introduce regularisation and optimisation based on the minimum‐energy criterion and show that this improves the processing output significantly. Analysis of ultra‐high‐resolution field data in conjunction with modelling shows that even relatively calm sea state (i.e., 15 cm wave height) introduces significant changes in the seismic signal for ultra‐high‐frequency band. These changes in amplitude and arrival time may degrade the results of deghosting. Using the field dataset, we show how the minimum‐energy optimisation of deghosting parameters improves the processing result. 相似文献
147.
ABSTRACT In this study, a two-stage fuzzy-stochastic factorial analysis (TFFA) method is developed and applied to the Vakhsh watershed (upper reaches of Aral Sea basin, Central Asia) for daily streamflow simulation. TFFA has advantages in identifying the major parameters that have important individual and interactive effects on model outputs, as well as assessing multiple uncertainties resulting from randomness and vagueness characteristics of model parameters. The results reveal that (a) nine major parameters (from a total of 24) have significant effects on Soil Water Assessment Tool (SWAT) simulation performance for the study watershed; and (b) snowmelt-related parameters (including snowfall temperature, threshold temperature for snowmelt and s nowmelt factor) and runoff curve number (CN2) are most sensitive parameters for the runoff generation. The results also show that the proposed TFFA method can help enhance the hydrological model’s capability for runoff simulation/prediction, particularly for in data-scarce and high-mountainous watersheds. 相似文献
148.
光滑地面毁伤程度评估非常重要,目前评估方法主要是基于光学图像,可是在恶劣的气候或环境条件下,很难获得光学图像,合成孔径雷达(SAR)克服了这个缺点. SAR图像是地表结构和电特征等地球物理参数的映射,通过SAR图像反演地表结构参数,可以推测出光滑地面毁伤程度. 本文以几何光学模型(GOM)为基础,建立神经网络反演模型,以获得光滑地表面受损后的粗糙度参数:表面均方根高度(σ)和表面相关长度(l),并进一步评估光滑地表面受损程度. 实验结果表明该方法可行. 相似文献
149.
J. A. Vargas-Guzmán T.-C. Jim Yeh 《Stochastic Environmental Research and Risk Assessment (SERRA)》1999,13(6):416-435
A sequential linear estimator is developed in this study to progressively incorporate new or different spatial data sets
into the estimation. It begins with a classical linear estimator (i.e., kriging or cokriging) to estimate means conditioned
to a given observed data set. When an additional data set becomes available, the sequential estimator improves the previous
estimate by using linearly weighted sums of differences between the new data set and previous estimates at sample locations.
Like the classical linear estimator, the weights used in the sequential linear estimator are derived from a system of equations
that contains covariances and cross-covariances between sample locations and the location where the estimate is to be made.
However, the covariances and cross-covariances are conditioned upon the previous data sets.
The sequential estimator is shown to produce the best, unbiased linear estimate, and to provide the same estimates and variances
as classic simple kriging or cokriging with the simultaneous use of the entire data set. However, by using data sets sequentially,
this new algorithm alleviates numerical difficulties associated with the classical kriging or cokriging techniques when a
large amount of data are used. It also provides a new way to incorporate additional information into a previous estimation. 相似文献
150.
Chris Roth 《Mathematical Geology》2000,32(3):277-300
Some years ago, earth scientists came to realize that knowing more about the geology of an orebody or an oil reservoir makes it easier to make the appropriate decisions concerning mine planning or reservoir exploitation. Geostatistical techniques for simulating lithofacies—that is, the geometry of the geology—were developed as a result of this. These methods should be able to produce geological images that respect not only the anisotropies of the different lithofacies but also their spatial layout relative to one another. While indicator variograms ensure that anisotropies are respected, another tool needs to be incorporated in the simulation technique to reflect the relative spatial layout of the different lithofacies. We propose to use the concept of edge effects that define the position of one lithofacies relative to another. Simple tests using direct and cross indicator variograms confirm the presence or absence of edge effects. We investigate if and how edge effect information can be incorporated in the different indicator simulation techniques—sequential indicator simulations, simulated annealing, the truncated Gaussian method and plurigaussian simulations. Results show that the choice of simulation method must be guided by the edge effect characteristics of the experimental lithologic data. 相似文献