A data exploration framework for validation and setup of hydrological models

Over the course of hydrological research projects often a large number of heterogeneous data sets are acquired from sources as diverse as boreholes, gauging stations or satellite imagery. This data then need to be integrated into models for the simulation of hydrological processes. We propose a framework for exploration of geoscientific data and visually guided preparation of such models. Data sets from a large number of sources can be imported, combined and validated to avoid potential problems due to artefacts or inconsistencies between data sets in a subsequent simulation. Boundary conditions and domain discretisations for surface and subsurface models can be created and tested regarding criteria indicating possible numerical instabilities. All data sets including simulation results can be integrated into a user-controlled 3D scene and aspects of the data can be enhanced using a number of established visualisation techniques including thresholding and user-defined transfer functions. We present the application of this framework for the preparation of a model for simulation of groundwater flow in a river catchment in southwest Germany investigated in the scope of the WESS project.     

Visual data exploration for hydrological analysis

Hydrological research projects for integrated water resources management such as the IWAS initiative often accumulate large amounts of heterogeneous data from different sources. Given the number of partners taking part in such projects, surveying and accessing the available data sets, as well as searching for a defined subset, becomes increasingly difficult. We propose an integrated approach for a system combining visual data management and numerical simulation which allows to survey and select data sets based on keywords such as a region of interest or given indicators. An adequate 3D visualisation of such subsets helps to convey information and significantly supports the assessment of relations between different types of data. Furthermore, the interface between the visual data management system and finite element codes allows for the straightforward integration of information into the numerical simulation process and the subsequent visualisation of simulation results in a geographical context. We demonstrate typical workflows for integration and processing within the system based on data from the IWAS model region in Saudi Arabia and the TERENO Bode Observatory in the Harz Mountains in Germany. In addition, we present examples for data import and export based on established standard file formats.     

Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis

GIS-based spatial data integration tasks for predictive geological applications, such as landslide susceptibility analysis, have been regarded as one of the primary geological application issues of GIS. An efficient framework for proper representation and integration is required for this kind of application. This paper presents a data integration framework based on the Dempster-Shafer theory of evidence for landslide susceptibility mapping with multiple geospatial data. A data-driven information representation approach based on spatial association between known landslide occurrences and input geospatial data layers is used to assign mass functions. After defining mass functions for multiple geospatial data layers, Dempster’s rule of combination is applied to obtain a series of combined mass functions. Landslide susceptibility mapping using multiple geospatial data sets from Jangheung in Korea was conducted to illustrate the application of this methodology. The results of the case study indicated that the proposed methodology efficiently represented and integrated multiple data sets and showed better prediction capability than that of a traditional logistic regression model.     

环境模拟和GIS集成的初步研究

GIS与环境模拟在技术、研究内容、方法上的进一步集成,具有广泛的应用前景.以开展的流域土壤和水资源研究模型的集成和系统化及其应用项目的实际工作,说明了环境模拟和GIS技术集成的必然性;从GIS技术的发展,环境模拟模型的改进与完善,数据的初始化与管理等方面系统地论述了环境模拟模型和GIS集成的必要性.通过现有集成模式的分析,对二者集成的概念框架以及不同的集成方式进行了概念性的描述,结合研究项目给出了集成示例.     

Multivariate Modelling of Geometallurgical Variables by Projection Pursuit

The integration of geological and geometallurgical data can significantly improve decision-making and optimize mining production due to a better understanding of the resources and their metallurgical performances. The primary-response rock property framework is an approach to the modelling of geometallurgy in which quantitative and qualitative primary properties are used as proxies of metallurgical responses. Within this framework, primary variables are used to fit regression models to predict metallurgical responses. Whilst primary rock property data are relatively abundant, metallurgical response property data are not, which makes it difficult to establish predictive response relationships. Relationships between primary input variables and geometallurgical responses are, in general, complex, and the response variables are often non-additive which further complicates the prediction process. Consequently, in many cases, the traditional multivariate linear regression models (MLR) of primary-response relationships perform poorly and a better alternative is required for prediction. Projection pursuit is a powerful exploratory statistical modelling technique in which data from a number of variables are projected onto a set of directions that optimize the fit of the model. The purpose of the projection is to reveal underlying relationships. Projection pursuit regression (PPR) fits standard regression models to the projected data vectors. In this paper, PPR is applied to the modelling of geometallurgical response variables. A case study with six geometallurgical variables is used to demonstrate the modelling approach. The results from the proposed PPR models show a significant improvement over those from MLR models. In addition, the models were bootstrapped to generate distributions of feasible scenarios for the response variables. Our results show that PPR is a robust technique for modelling geometallurgical response variables and for assessing the uncertainty associated with these variables.     

Multiscale insights into classical geomechanics problems

We pay a revisit to some classical geomechanics problems using a novel computational multiscale modelling approach. The multiscale approach employs a hierarchical coupling of the finite element method (FEM) and the discrete element method. It solves a boundary value problem at the continuum scale by FEM and derives the material point response from the discrete element method simulation attached to each Gauss point of the FEM mesh. The multiscale modelling framework not only helps successfully bypass phenomenological constitutive assumptions as required in conventional modelling approaches but also facilitates effective cross‐scale interpretation and understanding of soil behaviour. We examine the classical retaining wall and footing problems by this method and demonstrate that the simulating results can be well validated and verified by their analytical solutions. Furthermore, the study sheds novel multiscale insights into these classical problems and offers a new tool for geotechnical engineers to design and analyse geotechnical applications based directly upon particle‐level information of soils. Copyright © 2015 John Wiley & Sons, Ltd.     

Towards a geocomputational landscape epidemiology: surveillance,modelling, and interventions

The ability to explicitly represent infectious disease distributions and their risk factors over massive geographical and temporal scales has transformed how we investigate how environment impacts health. While landscape epidemiology studies have shed light on many aspects of disease distribution and risk differentials across geographies, new computational methods combined with new data sources such as citizen sensors, global spatial datasets, sensor networks, and growing availability and variety of satellite imagery offer opportunities for a more integrated approach to understanding these relationships. Additionally, a large number of new modelling and mapping methods have been developed in recent years to support the adoption of these new tools. The complexity of this research context results in study-dependent solutions and prevents landscape approaches from deeper integration into operational models and tools. In this paper we consider three common research contexts for spatial epidemiology; surveillance, modelling to estimate a spatial risk distribution and the need for intervention, and evaluating interventions and improving healthcare. A framework is proposed and a categorization of existing methods is presented. A case study into leptospirosis in Sri Lanka provides a working example of how the different phases of the framework relate to real research problems. The new framework for geocomputational landscape epidemiology encompasses four key phases: characterizing assemblages, characterizing functions, mapping interdependencies, and examining outcomes. Results from Sri Lanka provide evidence that the framework provides a useful way to structure and interpret analyses. The framework reported here is a new way to structure existing methods and tools of geocomputation that are increasingly relevant to researchers working on spatially explicit disease-landscape studies.     

Conditional Latin Hypercube Simulation of (Log)Gaussian Random Fields

In earth and environmental sciences applications, uncertainty analysis regarding the outputs of models whose parameters are spatially varying (or spatially distributed) is often performed in a Monte Carlo framework. In this context, alternative realizations of the spatial distribution of model inputs, typically conditioned to reproduce attribute values at locations where measurements are obtained, are generated via geostatistical simulation using simple random (SR) sampling. The environmental model under consideration is then evaluated using each of these realizations as a plausible input, in order to construct a distribution of plausible model outputs for uncertainty analysis purposes. In hydrogeological investigations, for example, conditional simulations of saturated hydraulic conductivity are used as input to physically-based simulators of flow and transport to evaluate the associated uncertainty in the spatial distribution of solute concentration. Realistic uncertainty analysis via SR sampling, however, requires a large number of simulated attribute realizations for the model inputs in order to yield a representative distribution of model outputs; this often hinders the application of uncertainty analysis due to the computational expense of evaluating complex environmental models. Stratified sampling methods, including variants of Latin hypercube sampling, constitute more efficient sampling aternatives, often resulting in a more representative distribution of model outputs (e.g., solute concentration) with fewer model input realizations (e.g., hydraulic conductivity), thus reducing the computational cost of uncertainty analysis. The application of stratified and Latin hypercube sampling in a geostatistical simulation context, however, is not widespread, and, apart from a few exceptions, has been limited to the unconditional simulation case. This paper proposes methodological modifications for adopting existing methods for stratified sampling (including Latin hypercube sampling), employed to date in an unconditional geostatistical simulation context, for the purpose of efficient conditional simulation of Gaussian random fields. The proposed conditional simulation methods are compared to traditional geostatistical simulation, based on SR sampling, in the context of a hydrogeological flow and transport model via a synthetic case study. The results indicate that stratified sampling methods (including Latin hypercube sampling) are more efficient than SR, overall reproducing to a similar extent statistics of the conductivity (and subsequently concentration) fields, yet with smaller sampling variability. These findings suggest that the proposed efficient conditional sampling methods could contribute to the wider application of uncertainty analysis in spatially distributed environmental models using geostatistical simulation.     

Modelling Spatial Variability Along Drainage Networks with Geostatistics

Local characteristics of drainage networks such as cross-section geometry and hydraulic roughness coefficient, influence surface water transfers and must be taken into account when assessing the impact of human activities on hydrological risks. However, as these characteristics have not been available till now through remote sensing or hydrological modelling, the only available methods are interpolation or simulation based on scarce data. In this paper we propose a statistical model based on geostatistics that allows us to take account of both the spatial distribution and spatial uncertainties. To do this, we modify the geostatistical framework to suit directed tree supports corresponding to drainage network structures. The stationarity concept is specified assuming conditional independence between parts of the network; variogram fitting and modelling are then modified accordingly. A sequential multi Gaussian simulation procedure going upstream along the network is proposed. We illustrate this approach by studying the width of an 11-km long artificial drainage network in the south of France.     

A Spatial Clustering Approach for Stochastic Fracture Network Modelling

Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for  fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach followed by a real 3D case study of the Habanero reservoir in the Cooper Basin, Australia.     

隐伏矿体三维综合信息成矿预测方法

开展三维综合信息成矿预测,是当前隐伏矿体找矿勘探的实际需要,但目前尚缺乏系统的研究,工作方法体系仍有待深入探讨。本文提出了一套较为完善的隐伏矿体三维综合信息定量预测流程和方法。方法包括数据收集及地质数据库管理、三维地质建模及地球物理数据融合、地质特征空间分析及控矿因素提取、多维多元控矿信息融合及预测信息集构建、隐伏矿体三维定位定量预测等多方面内容。由于方法综合了地质体三维建模、多维空间分析技术、地球物理方法以及预测方法,因此可有效提高三维成矿预测的有效性和可靠程度。为验证方法的有效性,本文在宁芜盆地分别针对矿田和矿区尺度,开展了中、大比例尺的三维成矿预测实例研究,取得了较好效果。研究显示该方法体系可有效地对深部隐伏矿体进行定位定量预测,能够服务于今后的新老矿区隐伏矿体找矿勘探工作。     

Whither stratigraphy?

There have been three revolutions in sedimentary geology. The first two began in the 1960s, consisting of the development of process-response sedimentary models and the application of plate-tectonic concepts to large-scale aspects of basin analysis. The third revolution, that of sequence stratigraphy, began in the late 1970s and helped to draw together the main results of the first two: the knowledge of autogenic processes learned through facies analysis, and the understanding of tectonism implicit in the unravelling of regional plate kinematics. Developments in the use of seismic-reflection data and the evaluation of a hypothesis of global eustasy provided considerable stimulation for stratigraphic research.Current developments in the field of sequence stratigraphy are focusing on three areas. (1) Elaboration of the sequence-architecture models for various configurations of depositional environment and sea-level history. (2) Exploration of various mechanisms for sequence generation, especially tectonism and orbital forcing. (3) Attempts to improve the level of precision in stratigraphic correlation and to refine the geological time scale, as a means to test the model of global eustasy.The growth in the power of computers and our knowledge of physical and chemical processes has led to the evolution of an entirely new way of evaluating earth history, termed quantitative dynamic stratigraphy. Mathematical modelling and numerical simulation of complex earth processes are now possible, and require the collection and integration of a wide array of quantitative and qualitative data sets. Applications include the study of the geodynamic evolution of sedimentary basins, modelling of stratigraphic sequences and global climates, studies of Milankovitch cycles (cyclostratigraphy) and simulation of fluid flow through porous media. The Global Sedimentary Geology Program has brought many of these areas of study together in multidisciplinary, global-scale studies of the sedimentary history of the earth. The results of these studies have wide application to many problems of importance to the human condition, including the past history of global climate change and other environmental concerns. The study of stratigraphy is at the centre of the new view of the earth, termed earth-systems science, which views earth as an ‘organic’ interaction between the lithosphere, biosphere, hydrosphere, and atmosphere.     

Accessing Australian geodynamic information: The AGCRC website

The Australian Geodynamics Cooperative Research Centre (AGCRC) website <http://www.agcrc. csiro.au/> provides the main integrated presentation of the results of AGCRC research. It hosts a systematic presentation of project summaries, publication abstracts, regionally organised geodynamic syntheses and a GIS database containing a large number of broad‐scale earth science datasets from the Australian region, which are presented through an interactive map viewer. Effective interfaces are required to make the variety of content accessible to users. In particular, maintaining effective hyperlinking for a large and growing set of pages was a significant challenge. We found that by developing effective indexing tools we were able to apply a uniform technology to attack these problems. The index included both the full text of the pages from the website and also specific index terms of metadata. The metadata schema was designed to support both generalised discovery through a search interface and also to allow hyperlinks to be included automatically in pages by the server, so that the user could browse in a richly cross‐linked framework. It was necessary to manage textual and numeric data within the same framework because even the textual documents were generally tied to geospatial locations identified by their geographical coordinates. This required extensions to standard index and search tools, including the development of complete new indexing software. To assist the user in accessing the index, we provided a variety of user interfaces specialised for different themes in the website. For example, publication lists are generated in a conventional layout, and geospatial datasets are presented through an interactive map viewer and query system. The latter also demonstrated how visualisation tools for geospatial data can be provided in the web‐browser interface.     

Geological modeling and simulation of CO2 injection in the Johansen formation

The Johansen formation is a candidate site for large-scale CO₂ storage offshore of the south-western coast of Norway. An overview of the geology for the Johansen formation and neighboring geological formations is given, together with a discussion of issues for geological and geophysical modelling and integrated fluid flow modelling. We further describe corresponding simulation models. Major issues to consider are capacity estimation and processes that could potentially cause CO₂ to leak out of the Johansen formation and into the formations above. Currently, these issues can only be investigated through numerical simulation. We consider the effect of different boundary conditions, sensitivity with respect to vertical grid refinement and permeability/transmisibility data, and the effect of residual gas saturations, since these strongly affect the CO₂-plume distribution. The geological study of the Johansen formation is performed based on available seismic and well data. Fluid simulations are performed using a commercial simulator capable of modelling CO₂ flow and transport by simple manipulation of input files and data. We provide details for the data and the model, with a particular focus on geology and geometry for the Johansen formation. The data set is made available for download online.     

人工神经网络模型在地学研究中的应用进展

近年来,随着人工神经网络（ANNs）自身技术的不断完善,应用ANNs模型成功解决各类地学问题的案例大量出现。通过对其发展历程进行分析发现,20世纪80年代末国际地学分析中已开始融入ANNs技术,国内则滞后 1～2年。在地学分析中使用的各类人工神经网络类型中,BP模型应用最广,占到85％以上。在10余年的应用过程中,虽然地学的各个分支学科都移植了一种或数种ANNs模型作为其分析工具,但水文、地质、大气、遥感等领域应用较为广泛。传统地学定量分析中的单变量或多变量预测成为人工神经网络地学模型的主要应用客体。同时,诸如模式识别和过程模拟等也是ANNs模型求解的对象。目前,随着建模经验和知识的积累,地学ANNs模型的发展呈现出多种技术综合集成的态势,遗传算法、小波转换、模拟退火算法以及模糊逻辑等方法与ANNs模型融合,成为解决地学分析中非线性问题的利器。     

TESSIN VISLab—laboratory for scientific visualization

Scientific visualization is an integral part of the modeling workflow, enabling researchers to understand complex or large data sets and simulation results. A high-resolution stereoscopic virtual reality (VR) environment further enhances the possibilities of visualization. Such an environment also allows collaboration in work groups including people of different backgrounds and to present results of research projects to stakeholders or the public. The requirements for the computing equipment driving the VR environment demand specialized software applications which can be run in a parallel fashion on a set of interconnected machines. Another challenge is to devise a useful data workflow from source data sets onto the display system. Therefore, we develop software applications like the OpenGeoSys Data Explorer, custom data conversion tools for established visualization packages such as ParaView and Visualization Toolkit as well as presentation and interaction techniques for 3D applications like Unity. We demonstrate our workflow by presenting visualization results for case studies from a broad range of applications. An outlook on how visualization techniques can be deeply integrated into the simulation process is given and future technical improvements such as a simplified hardware setup are outlined.     

Finite element simulation of the thermoviscoplastic behaviour of bituminous concrete

The general framework of the paper deals with the finite element modelling of thermomechanical problems involving viscous materials. The study focuses on the statement of constitutive equations describing the thermoviscoplastic behaviour of bituminous concrete, as well as on their implementation in a finite element program. After stating the general equations of the space- and time-continuous problem and the constitutive relations governing the viscoplastic component of the bituminous concrete behaviour, we deal with their integration over finite time steps, considering two different schemes. Eventually, two sets of numerical results are presented. The first one, an homogeneous triaxial test, is used to compare those schemes, whereas the second one consists of numerical simulations of real-size experiments performed on a road structure subjected to thermal and mechanical loadings. By comparing the numerical results with experimental ones, it allows us to test the finite element code on a more complex and realistic problem. Copyright © 1999 John Wiley & Sons Ltd.     

耦合湿地模块的流域水文模型模拟效率评价

耦合湿地模块的流域水文模型是开展流域湿地变化的水文效应及其水文功能评估的有效工具,其模拟效率直接关系到模拟精度和应用价值。选取中国湿地主要分布区之一的嫩江流域,利用PHYSITEL/HYDROTEL模型平台,构建孤立湿地和河滨湿地模块并与流域水文模型耦合,并从拟合优度指数和模拟效率角度评价了耦合湿地模块的流域水文模型模拟效率。结果表明,耦合湿地模块的流域水文模型,拟合和验证期间模型的拟合度指数均有所提高,嫩江富拉尔基和大赉水文站控制流域的Nash-Sutcliffe系数和克林效率系数平均提高了3.08%和4.64%,均方根误差和相对偏差平均降低了12.72%和55.93%,且整个研究时段模拟结果的流量指数总体更接近于观测的径流数据。可见,耦合湿地模块的流域水文模型可提高水文过程模拟精度,更好地定量评估湿地水文功能,为流域水资源精准调控与湿地修复保护提供重要支撑。     

Gradient-based Pareto optimal history matching for noisy data of multiple types

The advantages of the simultaneous integration of production and time-lapse seismic data for history matching have been demonstrated in a number of previous studies. Production data provide accurate observations at particular spatial locations (wells), while seismic data enable global, though filtered/noisy, estimates of state variables. In this work, we present an efficient computational tool for bi-objective history matching, in which data misfits for both production and seismic measurements are minimized using an adjoint-gradient approach. This enables us to obtain a set of Pareto optimal solutions defining the optimal trade-off between production and seismic data misfits (which are, to some extent, conflicting). The impact of noise structure and noise level on Pareto optimal solutions is investigated in detail. We discuss the existence of the “best” trade-off solution, or least-conflicting posterior model, which corresponds to the history-matched model that is expected to provide the least-conflicting forecast of future reservoir performance. The overall framework is successfully applied in 2D and 3D compositional simulation problems to provide a single least-conflicting posterior model and, for the 2D case, multiple samples from the posterior distribution using the randomized maximum likelihood method.