共查询到20条相似文献，搜索用时 31 毫秒
1.
Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data. 相似文献
2.
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
3.
Spatial uncertainty analysis is a complex and difficult task for orebody estimation in the mining industry. Conventional models (kriging and its variants) with variogram-based statistics fail to capture the spatial complexity of an orebody. Due to this, the grade and tonnage are incorrectly estimated resulting in inaccurate mine plans, which lead to costly financial decision. Multiple-point geostatistical simulation model can overcome the limitations of the conventional two-point spatial models. In this study, a multiple-point geostatistical method, namely SNESIM, was applied to generate multiple equiprobable orebody models for a copper deposit in Africa, and it helped to analyze the uncertainty of ore tonnage of the deposit. The grade uncertainty was evaluated by sequential Gaussian simulation within each equiprobable orebody models. The results were validated by reproducing the marginal distribution and two- and three-point statistics. The results show that deviations of volume of the simulated orebody models vary from ? 3 to 5% compared to the training image. The grade simulation results demonstrated that the average grades from the different simulation are varied from 3.77 to 4.92% and average grade 4.33%. The results also show that the volume and grade uncertainty model overestimates the orebody volume as compared to the conventional orebody. This study demonstrates that incorporating grade and volume uncertainty leads to significant changes in resource estimates. 相似文献
4.
David J. Magutre 《International journal of geographical information science》2013,27(4):487-488
The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature manipulation engines (FMEs), ontology-driven data integration and spatial mediators have been proposed. The major aim of these initiatives is to harmonize data sets and establish interoperability between different data sources. On the contrary, spatial data integration and interoperability is not a pure technical exercise, and there are other nontechnical issues including institutional, policy, legal and social issues involved. Spatial Data Infrastructure (SDI) framework aims to better address the technical and nontechnical issues and facilitate data integration. The SDIs aim to provide a holistic platform for users to interact with spatial data through technical and nontechnical tools. This article aims to discuss the complexity of the challenges associated with data integration and propose a tool that facilitates data harmonization through the assessment of multisource spatial data sets against many measures. The measures represent harmonization criteria and are defined based on the requirement of the respective jurisdiction. Information on technical and nontechnical characteristics of spatial data sets is extracted to form metadata and actual data. Then the tool evaluates the characteristics against measures and identifies the items of inconsistency. The tool also proposes available manipulation tools or guidelines to overcome inconsistencies among data sets. The tool can assist practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration. 相似文献
5.
Soil erosion is a major threat to sustainable agriculture. Evaluating regional erosion risk is increasingly needed by national and in-ternational environmental agencies. This study elaborates a model (using spatial principal component analysis [SPCA]) method for the evaluation of soil erosion risk in a representative area of dry-hot valley (Yuanmou County) at a scale of 1:100,000 using a spatial database and GIS. The model contains seven factors: elevation, slope, annual precipitation, land use, vegetation, soil, and population density. The evaluation results show that five grades of soil erosion risk: very low, low, medium, high, and very high. These are divided in the study area, and a soil erosion risk evaluation map is created. The model may be applicable to other areas of China because it utilizes spatial data that are generally available. 相似文献
6.
Original field data reports from the Azerbaijan sector of the South Caspian Basin have been used to compile statistical histograms of reservoir characteristics for both onshore and offshore oil fields. Two groups of statistics are presented here: the first group discusses reservoir thickness, areas, volumes, and horizon depths for the onshore and offshore fields; the second group discusses permeability, porosity, oil viscosity, oil recovery factor, reserves, and production for the onshore and offshore fields. These statistical distributions have been constructed so that one has available an historical database for use in assessing the range of likely reservoir characteristics in exploration ventures in this basin. 相似文献
7.
Paul H. Jung Jean-Claude Thill Michele Issel 《International journal of geographical information science》2019,33(6):1155-1175
ABSTRACTWe argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran’s I statistics estimated on 5-year ACS estimates (2006–2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data. 相似文献
8.
A Methodology for the Assessment of Unconventional (Continuous) Resources with an Application to the Greater Natural Buttes Gas Field,Utah 总被引：1，自引：0，他引：1
The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah,
that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended
to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery
from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters
(BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it
is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated
drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of
constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence,
statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage
of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations
observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell
EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to
model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide
multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation
of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources,
which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A
second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which
there is no production information but which is regarded as being similar to Greater Natural Buttes. 相似文献
9.
甘肃灌漠土土壤肥力的空间变异性典型研究 总被引：12，自引：7，他引：12
结合传统统计与地统计学方法对甘肃省河西地区武威灌漠土土壤肥力的空间变异性进行了分析。描述性统计结果显示，该地土壤肥力具有较大的空间变异，土壤NH4^ -N的变异系数最高，为62．656％，速效磷的变异系数次之，为47．369％，pH的变异系数最小，仅为1．284％，有机质和速效钾的变异系数分别为13．208％和38．075％。地统计学分析表明该区域土壤特性的块金方差／基台值比值[C0／(C0 C)]在0．0413％～13．7801％之间，说明该土壤性质的各项指标均有强烈的空间相关性，其中NH 一N和pH值的空间相关性相对最强，[C。／(G C)]分别为0．0413％和0．0978％；速效磷相对最弱，[C0／(C0 C)]为13．7801％；有机质与速效钾居中。土壤肥力性质的相关距变化范围为176～294m。该土壤性质的克立格插值结果显示出一定的空间相似性，显示地统计学方法可用于分析土壤肥力在空间上的变异。 相似文献
10.
11.
R. Sierra C. R. Stephens 《International journal of geographical information science》2013,27(3):441-468
Visual data mining of spatial data is a challenging task. As exploratory analysis is fundamental, it is beneficial to explore the data using different potential visualisations. In this article, we propose and analyse network graphs as a useful visualisation tool to mine spatial data. Due to their ability to represent complex systems of relationships in a visually insightful and intuitive way, network graphs offer a rich structure that has been recognised in many fields as a powerful visual representation. However, they have not been sufficiently exploited in spatial data mining, where they have principally been used on data that come with an explicit pre-specified network graph structure. This research presents a methodology with which to infer relationship network graphs for large collections of boolean spatial features. The methodology consists of four principal stages: (1) define a co-location model, (2) select the type of co-association of interest, (3) compute statistical diagnostics for these co-associations and (4) construct and visualise a network graph of the statistic from step (3). We illustrate the potential usefulness of the methodology using an example taken from an ecological setting. Specifically, we use network graphs to understand and analyse the potential interactions between potential vector and reservoir species that enable the propagation of leishmaniasis, a disease transmitted by the bite of sandflies. 相似文献
12.
13.
Lianfa Li Corresponding author Jinfeng Wang Chengyi Wang 《International journal of geographical information science》2013,27(3):363-384
In disaster insurance and reinsurance, GIS has been used to visualize and manage geospatial data and to help vulnerability and risk analysis for years. However, hazard insurance is a multidisciplinary issue that involves complex factors and uncertainty. GIS, if used alone, has limited functionality due to poor incorporation of intelligence and spatial statistics. The Spatial Decision Support System (SDSS) presented in this paper, addresses some of the deficiencies of traditional GIS, by providing powerful tools to support disaster insurance pricing that involves procedural and declarative knowledge. In the SDSS, the knowledge‐based system shell, using the open‐source CLIPS and supporting fuzziness and uncertainty, can be applied in at least three phases: hazard simulation, fuzzy comprehensive evaluation of risk, and query for insurance pricing. The libraries of statistics and spatial statistics provide a robust support for analysis of spatial factors, including spatial correlation between zones vulnerable to hazard and spatial variation of exposures. The GIS components provide sophisticated visualization and database management support for geospatial data, helping easily locate the insured points and risk zones as well as exploratory analysis of spatial data. Standard database management interfaces are used to manage other aspatial data. COM, an industry‐wide interface protocol, tightly integrates these technologies (the expert shell, GIS, spatial statistics and DBM within an integral system), and can be used to develop mixed complex algorithms in support of other COM objects. An application of typhoon insurance pricing is demonstrated with a case study in Guangdong, China. Developed as a suite of generic tools with abilities to deal with the complex problem of disaster insurance involving spatial factors and field knowledge, this prototype SDSS can also be applied to other disaster insurance and fields that involve similar spatial decision making. 相似文献
14.
Dr Helen Mounsey 《International journal of geographical information science》2013,27(3):259-261
Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail. 相似文献
15.
16.
JOHN WILSON 《International journal of geographical information science》2013,27(4):355-357
The past decade has witnessed extensive development of measures that examine characteristics of spatial subsets (local spaces) defined with respect to a complete data set (global space). Such procedures have evolved independently in fields such as geography, GIS, cartography, remote sensing, and landscape ecology. Collectively, we label these procedures as local spatial methods. We focus on those methods that share a common goal of identifying subsets whose characteristics are statistically ‘significant’ in some way. We propose the concept of local spatial statistical analysis (LoSSA) both as an integrative structure for existing methods and as a framework that facilitates the development of new local and global statistics. By formalizing what is involved when a particular local statistic is used, LoSSA helps to reveal the key features and limitations of the procedure. These include a consideration of the nature of the spatial subsets, their spatial relationship to the complete data set, and the relationship between a given global statistic and the corresponding local statistics computed for the data set. 相似文献
17.
The Athabasca oil sands deposit, Alberta, Canada, is one of the largest known hydrocarbon accumulations. The efficient exploitation of this deposit, as well as other oil sand accumulations throughout the world, is based onin situ recovery and surface mining methods. Quantitative modeling of deposit heterogeneity provides a valuable engineering tool. In the present study, conditional simulation was used to model oil-saturated zones in part of the Athabasca deposit. This technique generates equiprobable models of thein situ variability of essential deposit attributes that honor the available data and their spatial statistics. The application of the technique is based on the delineation of geologically homogeneous zones within the host McMurray Formation, their statistical validity, and the integration of geological interpretations. The geological framework is developed, and subsequently, high resolution conditionally simulated models of three identified hydrocarbon-bearing zones are generated, in terms of the zone boundaries and the percent weight of oil saturation. These models serve as “what-if” tools for risk assessment and future planning. 相似文献
18.
基于HASM的中国森林植被碳储量空间分布模拟 总被引：2，自引：1，他引：1
当前区域尺度上森林碳储量估算主要依据森林资源清查数据,整个过程不仅消耗大量人力、物力,而且十分耗时,严重影响了森林碳储量估算的时效性。针对这一问题,本文提出了基于HASM的森林植被碳储量模拟方法,该方法以全球植被动态模型LPJ-Guess 输出的植被碳储量为驱动场,以森林清查样地数据为精度控制点,模拟生成中国陆地森林碳储量分布情况。研究以第7 次中国森林资源清查数据作为精度控制点数据源,同时作为本文模拟方法的精度验证。结果表明,中国森林碳储量为9.2405 Pg,考虑到森林资源清查是基于一定的郁闭度进行的,因此HASM模拟的结果与根据森林资源清查结果计算得出的7.8115 Pg 相比更符合实际情况,西南山区和东北林区是中国森林最主要的碳库,其碳储量分别占中国森林植被碳储量的39.82%和20.46%。同时与之前(1975-1995 年) 相比具有较大幅度的增长,表明近几十年来中国坚持大规模植树造林的碳汇效果显著。同时也表明基于HASM的森林植被碳储量空间分布模拟方法是有效的,模拟结果合理且精度较高,表明该方法在全球尺度上森林植被碳储量模拟及其它生态系统中碳储量模拟中具有应用潜力。 相似文献
19.
Weiwei Duan Yao-Yi Chiang Stefan Leyk Johannes H. Uhl Craig A. Knoblock 《International journal of geographical information science》2020,34(4):824-849
ABSTRACTWith large amounts of digital map archives becoming available, automatically extracting information from scanned historical maps is needed for many domains that require long-term historical geographic data. Convolutional Neural Networks (CNN) are powerful techniques that can be used for extracting locations of geographic features from scanned maps if sufficient representative training data are available. Existing spatial data can provide the approximate locations of corresponding geographic features in historical maps and thus be useful to annotate training data automatically. However, the feature representations, publication date, production scales, and spatial reference systems of contemporary vector data are typically very different from those of historical maps. Hence, such auxiliary data cannot be directly used for annotation of the precise locations of the features of interest in the scanned historical maps. This research introduces an automatic vector-to-raster alignment algorithm based on reinforcement learning to annotate precise locations of geographic features on scanned maps. This paper models the alignment problem using the reinforcement learning framework, which enables informed, efficient searches for matching features without pre-processing steps, such as extracting specific feature signatures (e.g. road intersections). The experimental results show that our algorithm can be applied to various features (roads, water lines, and railroads) and achieve high accuracy. 相似文献
20.
地理学“空间分析导论”课程设置研究 总被引：1，自引：0，他引：1
空间分析是地理学和其他相关学科的重要研究手段与方法,其重要性日益引起关注。结合1960年代以来空间分析的发展脉络及各时期的典型方法,在对国内外40多所高校、组织与机构的空间分析课程大纲对比研究和教学实践的基础上,提出了“空间分析导论”课程的教学大纲、具体内容和实习软件等问题,最后对空间分析课程设置提出几点建议:① 拟定空间分析课程66学时左右,其中至少12学时左右的上机实习,实习软件可以针对不同的数据类型选用ArcGIS、GeoDa和R。② 在地理学相关专业或其他学科领域的本科高年级开设入门级的空间分析导论课程,之后,根据具体情况在研究生阶段开设比如"地统计学"、"空间模型与建模"等专题课程。③ 为进一步完善国内空间分析课程教材,可考虑选择引进国外相关著作,并筛选、开发相关的教学案例,编写详细的上机实习操作指导。 相似文献