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1.
Multiple-Point Statistics for Training Image Selection   总被引：2，自引：0，他引：2  
Selecting a training image (TI) that is representative of the target spatial phenomenon (reservoir, mineral deposit, soil type, etc.) is essential for an effective application of multiple-point statistics (MPS) simulation. It is often possible to narrow potential TIs to a general subset based on the available geological knowledge; however, this is largely subjective. A method is presented that compares the distribution of runs and the multiple-point density function from available exploration data and TIs. The difference in the MPS can be used to select the TI that is most representative of the data set. This tool may be applied to further narrow a suite of TIs for a more realistic model of spatial uncertainty. In addition, significant differences between the spatial statistics of local conditioning data and a TI may lead to artifacts in MPS. The utilization of this tool will identify contradictions between conditioning data and TIs. TI selection is demonstrated for a deepwater reservoir with 32 wells.  相似文献   

2.
This paper evaluates the application of geothermal energy by numerically modeling the heat extraction that would result from the injection of cold water into an artificially fractured hot dry rock (HDR). The HDR that would be utilized in Alberta is expected to be granite with a network of pre-existing natural fractures. However, to ensure a continued flow of injected water from the reservoir to the production wells, creation of additional fractures is required. Thus, the properties of these fractures are of prime importance to the efficiency of geothermal energy production. The fracture networks for the simulations were created using a numerical code and were converted into a grid format to be used in a commercial thermal simulator. A new approach to embed a complex fracture system into the numerical model was applied. Various properties of the fractures such as aperture, length, and spacing were changed and their absolute and relative effects on energy production were quantified and the results are presented in this paper. This modeling technique was also verified by comparison with the conventional dual porosity model and by performing a history match with real field data obtained from literature. The applicability of this approach to provide heat for oil sands extraction was investigated using the volumes of water currently needed in northern Alberta. Based on these constraints, numerical simulations were run to evaluate the optimum well spacing that would be required using a three-well configuration. In this simulation, the fracture parameters (density and aperture) were kept fixed assuming that they are not affected by cold water injection. The results of this study suggest that geothermal energy has a potential to be a sustainable form of thermal energy for oil sands extraction in northern Alberta.  相似文献   

3.
Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling.  相似文献   

4.
Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns.  相似文献   

5.
There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.  相似文献   

6.
Geostatistical models should be checked to ensure consistency with conditioning data and statistical inputs. These are minimum acceptance criteria. Often the first and second-order statistics such as the histogram and variogram of simulated geological realizations are compared to the input parameters to check the reasonableness of the simulation implementation. Assessing the reproduction of statistics beyond second-order is often not considered because the “correct” higher order statistics are rarely known. With multiple point simulation (MPS) geostatistical methods, practitioners are now explicitly modeling higher-order statistics taken from a training image (TI). This article explores methods for extending minimum acceptance criteria to multiple point statistical comparisons between geostatistical realizations made with MPS algorithms and the associated TI. The intent is to assess how well the geostatistical models have reproduced the input statistics of the TI; akin to assessing the histogram and variogram reproduction in traditional semivariogram-based geostatistics. A number of metrics are presented to compare the input multiple point statistics of the TI with the statistics of the geostatistical realizations. These metrics are (1) first and second-order statistics, (2) trends, (3) the multiscale histogram, (4) the multiple point density function, and (5) the missing bins in the multiple point density function. A case study using MPS realizations is presented to demonstrate the proposed metrics; however, the metrics are not limited to specific MPS realizations. Comparisons could be made between any reference numerical analogue model and any simulated categorical variable model.  相似文献   

7.
Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.  相似文献   

8.
The temporal dimensions of public transit accessibility have recently garnered an increasing amount of interest. However, the existing literature on transit accessibility is heavily based on oversimplified assumptions that transit services operate at deterministic speeds using predetermined timetables. These measurements may overestimate transit accessibility, especially for large metropolitan areas where inter- and intra-modal transfers are frequent. To handle travel time uncertainty, a multi-modal transit accessibility modeling approach is proposed to account for realistic variations in travel time and service reliability. The proposed approach is applied to the mapping of transit accessibility in Shenzhen (China), where transit services exhibit significant travel time variations over space and time. Compared to traditional transit accessibility measures, our method has been demonstrated to better capture intrinsic spatial and temporal accessibility variations with complex multi-modal transit networks. Normal distribution of inter-stop travel times and constant travel speed between GPS sampling points are assumed to simply the computation, which we consider to adjust in future studies to better quantify the dynamics of transit accessibility across space and time.  相似文献   

9.
The availability of spatial data on an unprecedented scale as well as advancements in analytical and visualization techniques gives researchers the opportunity to study complex problems over large urban and regional areas. Nevertheless, few individual data sets exist that provide both the requisite spatial and/or temporal observational frequency to truly facilitate detailed investigations. Some data are collected frequently over time but only at a few geographic locations (e.g., weather stations). Similarly, other data are collected with a high level of spatial resolution but not at regular or frequent time intervals (e.g., satellite data). The purpose of this article is to present an interpolation approach that leverages the relative temporal richness of one data set with the relative spatial richness of another to fill in the gaps. Because different interpolation techniques are more appropriate than others for specific types of data, we propose a space–time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem to increase the accuracy results.

We call our ensemble approach the space–time interpolation environment (STIE). The primary steps within this environment include a spatial interpolation processor, a temporal interpolation processor, and a calibration processor, which enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In this article, we first describe STIE conceptually including the data input requirements, output structure, details of the primary steps, and the mechanism for coordinating the data within those steps. We then describe a case study focusing on urban land cover in Phoenix, Arizona, using our working implementation. Our empirical results show that our approach increased the accuracy for estimating urban land cover better than a single interpolation technique.  相似文献   

10.

Interpolation of point measurements using geostatistical techniques such as kriging can be used to estimate values at non-sampled locations in space. Traditional geostatistics are based on the spatial autocorrelation concept that nearby things are more related than distant things. In this study, additional information was used to modify the traditional Euclidean concept of distance into an adjusted distance metric that incorporates similarity in terms of quantifiable landscape characteristics such as topography or land use. This new approach was tested by interpolating soil moisture content, pH and carbon-to-nitrogen (C:N) ratio measured in both the mineral and the organic soil layers at a field site in central Sweden. Semivariograms were created using both the traditional distance metrics and the proposed adjusted distance metrics to carry out ordinary kriging (OK) interpolations between sampling points. In addition, kriging with external drift (KED) was used to interpolate soil properties to evaluate the ability of the adjusted distance metric to incorporate secondary data into interpolations. The new adjusted distance metric typically lowered the nugget associated with the semivariogram, thereby better representing small-scale variability in the measured data compared to semivariograms based on the traditional distance metric. The pattern of the resulting kriging interpolations using KED and OK based on the adjusted distance metric were similar because they represented secondary data and, thus, enhanced small-scale variability compared to traditional distance OK. This created interpolations that agreed better with what is expected for the real-world spatial variation of the measured properties. Based on cross-validation error, OK interpolations using the adjusted distance metric better fit observed data than either OK interpolations using traditional distance or KED.  相似文献   

11.
SWAT分布式流域水文物理模型的改进及应用研究   总被引：33，自引：2，他引：31  
张东  张万昌  朱利  朱求安 《地理科学》2005,25(4):434-440
SWAT (Soil and Water Assessment Tool) 模型是一个集成遥感 (RS)、地理信息系统 (GIS) 和数字高程模型(DEM)技术的先进的分布式流域水文物理模型。为了推动该模型在中国的适应性研究及应用,并改进模型以提高水文模拟的精度,针对模型在中国西北寒旱区的黑河流域和中西部温润的汉江流域的水文模拟中发现的问题进行了扩充和改进,增加了土壤粒径转换模块和天气发生器(WGEN)数据预处理模块,改进了模型中的WGEN算法、潜在蒸散量模拟算法以及气象参数的空间离散方法。利用扩充和改进后的模型对汉江褒河上游江口流域的降雨-径流过程进行了系统的研究。结果表明,不仅模型的使用效率有明显提高,而且改进后模型的效率系数和相关系数也比改进前有较大改善。  相似文献   

12.
Urbanization is an important issue concerning diverse scientific and policy communities. Computational models quantifying locations and quantities of urban growth offer numerous environmental and socioeconomic benefits. Traditional urban growth models are based on a single-algorithm fitting procedure and thus restricted on their ability to capture spatial heterogeneity. Accordingly, a GIS-based modeling framework titled multi-network urbanization (MuNU) model is developed that integrates multiple neural networks. The MuNU model enables a filtering approach where input data patterns are automatically reallocated into appropriate neural networks with targeted accuracies. We hypothesize that observations classified by individual neural networks share greater homogeneity, and thus modeling accuracy will increase with the integration of multiple targeted algorithms. Land use and land cover data sets of two time snapshots (1977 and 1997) covering the Denver Metropolitan Area are used for model training and validation. Compared to a single-step algorithm – either a stepwise logistic regression or a single neural network – several improvements are evident in the visual output of the MuNU model. Statistical validations further quantify the superiority of the MuNU model and support our hypothesis of effective incorporation of spatial heterogeneity.  相似文献   

13.
杜云艳  易嘉伟  薛存金  千家乐  裴韬 《地理学报》2021,76(11):2853-2866
地理事件作为描述地理过程的基本单元,逐渐成为地理信息科学（GIS）核心研究内容。由于受人类活动数据获取限制,GIS对地理事件的建模和分析主要关注事件所引起的地理空间要素变化及要素之间的相互影响与作用机制。然而,近年来随着基于位置服务数据（LBS）爆炸式的增长和人类活动大数据定量刻画手段的快速发展,地理事件对人类活动的影响以及公众对地理事件的网络参与度都引起了多个领域的广泛关注,对地理事件的时空认知、建模方法和分析框架提出了巨大的挑战。对此,本文首先深入分析了大数据时代地理事件的概念与分类体系;其次,基于地理事件的时空语义给出了基于图模型的事件数据建模,建立了事件本体及其次生或级联事件的“节点—边”表达结构,开展了事件自身时空演化及其前“因”后“果”的形式化描述;第三,从时空数据分析与挖掘的角度,给出了大数据时代地理事件建模与分析的整体框架,拟突破传统“地理实体空间”事件探测与分析方法的局限性,融合“虚拟空间”事件发现与传播模拟思路,实现多源地理大数据支撑下的面向地理事件的人类活动多尺度时空响应与区域差异分析;最后,本文以城市暴雨事件为例诠释了本文所提出的地理事件建模与分析方法,从城市和城市内部两个尺度进行了暴雨事件与人类活动的一致性响应及区域差异分析,得到了明确的结论,验证了前文分析框架的可行性与实用性。  相似文献   

14.
To enhance the quality of oil- and gas-resource assessments and to reduce the risks in oil and gas exploration, a number of assessment techniques have been developed. Unfortunately, these techniques have not always been effective in the timely transfer of information. The amount of time that is required for preparing assessments does not always allow for the necessary high-quality data to be generated. To overcome this problem, a method based on an analysis of the phase state of oil and the dynamics of fluids in secondary migration of hydrocarbons is proposed. The phase state of the oil and fluid potential for secondary migration is estimated initially for each prospect together with the extent of the drainage area. On the basis of these estimates, statistical calculations can be made for the generation and expulsion of hydrocarbons. As a result, more reliable data are available for prospect assessment. The application of this method has a practical significance in that it brings the role of basin modeling in prospect assessment into full play, increases the reliability of petroleum-resource assessments, and reduces the risks in exploration. A case study from the Beitang region in eastern China is presented.  相似文献   

15.
In this paper, sparse data problem in neural network and geostatistical modeling for ore-grade estimation was addressed in the Nome offshore placer gold deposit. The problem of sparse data arises because of the random data division into training, validation, and test subsets during ore-grade modeling. In this regard, the possibility of generating statistically dissimilar data subsets by random data division was also explored through a simulation exercise. A combined approach of data segmentation and application of a Kohonen network then was used to solve the data division problem. Two neural networks and five kriging models were applied for grade modeling. The neural network was trained using an early stopping method. Performance evaluation of the models was carried out on the test data set. The study results indicated that all the models that were investigated in this study performed almost equally. It was also revealed that by using the secondary variable watertable depth the neural network and the kriging models slightly improved their prediction precision. Further, the overall R 2 of the models was poor as a result of high nugget (noisy) component in ore-grade variation.  相似文献   

16.
Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO2, H2S, N2 and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R2) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.  相似文献   

17.
Simple net model constructed by authors, facies analysis and compaction models, were applied to evaluate reservoir properties of sandstone facies of the Carpathian Flysch (the Istebna sandstones). The applied net model was built on the base of fractal approach proposed by Don Turcotte in 1977 and computer analysis of images. Laboratory measurements include porosity, density, permeability to nitrogen, mercury injection capillary pressure tests, and microscopic analysis of thin sections. D.W. Houseknecht's theory, proposed in 1987, was applied for compaction and cementation modeling. The residual saturation data were used to validate obtained results. Net model allows an evaluation of filtration properties of analyzed sandstones and to distinguish the classes of similarity of pore space. The extracted parameters of classes of similarity were correlated with facies scheme of the investigated geological structure. Influence of compaction and cementation on pore space parameters was discussed.  相似文献   

18.
This article reports the findings from simulating the spatial diffusion processes of memes over social media networks by using the approach of agent-based modeling. Different from other studies, this article examines how space and distance affect the diffusion of memes. Simulations were carried out to emulate and to allow assessment of the different levels of efficiency that memes spread spatially and temporally. Analyzed network structures include random networks and preferential attachment networks. Simulated spatial processes for meme diffusion include independent cascade models and linear threshold models. Both simulated and real-world social networks were used in the analysis. Findings indicate that the numbers of information sources and opinion leaders affect the processes of meme diffusion. In addition, geography is still important in the processes of spatial diffusion of memes over social media networks.  相似文献   

19.
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20.
基于案例推理的元胞自动机及大区域城市演变模拟   总被引：19，自引：0，他引：19  
黎夏  刘小平 《地理学报》2007,62(10):1097-1109
元胞自动机(CA) 被越来越多地用于复杂系统的模拟中。许多地理现象的演变与其影响要素之间存在着复杂的关系, 并往往具有时空动态性。在研究区域较大和模拟时间较长时, 定义具体的规则来反映这种复杂关系有较大的困难。为了解决CA 转换规则获取的瓶颈问题, 提出了基于案例推理(CBR) 的CA 模型, 并对CBR 的k 近邻算法进行了改进, 使其能反映转换规则的时空动态性。将该模型应用于大区域的珠江三角洲城市演变中。实验结果显示, 其模拟的空间格局与实际情况吻合较好。与常规的基于Logistic 的CA 模型进行了对比, 所获得的模拟结果有更高的精度和更接近实际的空间格局, 特别在模拟较为复杂的区域时有更好的模拟效果。  相似文献   

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