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object-based retrieval of biophysical canopy variables using artificial neural nets and radiative transfer models

REMOTE SENSING OF ENVIRONMENT 2004年第1-2期93卷 53-67页

作者： Atzberger, C CSE INRA Avignon F-84914 Avignon 9 France

The retrieval of biophysical variables using canopy reflectance models is hampered by the fact that the inverse problem is ill-posed. This leads to unstable and often inaccurate inversion results. In order to regularize the model inversion, a novel approach has been developed and tested on synthetic Landsat TM reflectance data. The method takes into account the neighbouring radiometric information of the pixel of interest, named object signature. The neighbourhood data can either be extracted from gliding windows, already segmented images, or using digitized field boundaries. The extracted radiometric data of the neighbourhood pixels are used to calculate 42 descriptive statistical properties that comprehensively characterize the spectral (co)variance of the image object (e.g. mean and standard deviation of the distributions, intercorrelations between spectral bands, etc.). Together with the habitual spectral signature of the pixel being inverted (6 variables), this object signature (42 variables) is used as input in an artificial neural net to estimate simultaneously three important biophysical variables (i.e. leaf area index, leaf chlorophyll, and leaf water content). The use of neural nets for the model inversion avoids time-consuming iterative optimizations and provides a computational effective way to consider simultaneously pixel and object signatures. In order to "learn'' the relation between spectral signatures and biophysical variables, the neural nets were previously trained on large synthetic data sets. The data sets consist of pixel signatures and the corresponding signatures of image objects representing various agricultural fields. The signatures were simulated with the SAILH+PROSPECT canopy reflectance model, assuming largely varying intra-and interfield distributions of the model input parameters. To demonstrate the benefits of the object-based inversion, neural nets were also trained on the pixel signatures alone. For this purpose, the object signat

关键词： radiative transfer model inverse problem regularization object-based modeling numerical experiment Landsat TM texture leaf area index leaf chlorophyll content equivalent leaf water thickness artificial neural net

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ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems

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COMPUTERS & GEOSCIENCES 2009年第8期35卷 1671-1685页

作者： Pyrcz, M. J. Boisvert, J. B. Deutsch, C. V. Chevron Energy Technol Co Quantitat Stratig Houston TX 77002 USA Univ Alberta Dept Civil & Environm Engn CNRL Nat Resources Engn Facil Edmonton AB T6G 2W2 Canada

This paper presents an algorithm for the construction of event-based fluvial models. The event-based approach may be applied to construct stochastic pseudo-process-based fluvial models for a variety of fluvial styles with conditioning to sparse well data (1-5 wells) and areal and vertical trends. The initial models are generated by placing large-scale features, such as channels and crevasse splays, into the model as geometric objects. These large-scale features are controlled by geometric input parameters provided by the user and are placed into the model to roughly honor well data through a rejection and updating method. Yet, some model to well data mismatch may still occur due to inconsistency in the size and positioning of complicated features relative to the well data. An image processing algorithm is used to post-process realizations to exactly honor all well data. The final, cell-based models, have no data mismatch and contain geologically realistic fluvial features that would be difficult to obtain with other pixel-based methods and precise conditioning that is difficult to obtain with object-based methods. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Reservoir characterization Geostatistics Facies modeling object-based modeling

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Stochastic surface modeling of deepwater depositional systems for improved reservoir models

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2009年第1-2期68卷 118-134页

作者： Zhang, X. Pyrcz, M. J. Deutsch, C. V. Roxar Canada Ltd Calgary AB Canada Chevron Energy Technol Co Earth Sci Res & Dev Houston TX USA Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada

High resolution reservoir models are often required for resource assessment and reservoir management. The reservoir models must reproduce geologically realistic heterogeneity that may affect predictions. An important heterogeneity in many deepwater depositional systems consists of facies variations controlled by geological events that lead to lobe geometries. A surface-based geostatistical scheme is presented to model high resolution lobes and their associated stacking patterns in a geometrically realistic manner. The lobes are constrained to match well data. Facies, porosity and permeability are then modeled within the lobe Application to a reservoir with 36 wells demonstrates the practical applicability of the approach. framework. This surface-based method may be readily extended to model other geometries in various reservoir settings. (C) 2009 Elsevier B.V. All rights reserved.

关键词： geostatistics reservoir management object-based modeling structural modeling heterogeneity quantification facies modeling

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Mapping salt marsh soil properties using imaging spectroscopy

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2019年 148卷 221-234页

作者： Zhang, Caiyun Mishra, Deepak R. Pennings, Steven C. Florida Atlantic Univ Dept Geosci Boca Raton FL 33431 USA Univ Georgia Dept Geog Ctr Geospatial Res Athens GA 30602 USA Univ Houston Dept Biol & Biochem Houston TX 77204 USA

Tidal salt marshes sequester and store blue carbon at both short and long time scales. Marsh soils shape and maintain the ecosystem by supporting complex biogeochemical reactions, deposition of sediment, and accumulation of organic matter. In this study, we examined the potential of imaging spectroscopy techniques to indirectly quantify and map tidal marsh soil properties at a National Estuarine Research Reserve in Georgia, USA. A framework was developed to combine modern digital image processing techniques for marsh soil mapping, including object-based image analysis (OBIA), machine learning modeling, and ensemble analysis. We also evaluated the efficacy of airborne hyperspectral sensors in estimating marsh soil properties compared to spaceborne multispectral sensors, WorldView-2 and QuickBird. The pros and cons of object-based modeling and mapping were assessed and compared with traditional pixel-based mapping methods. The results showed that the designed framework was effective in quantifying and mapping three marsh soil properties using the composite reflectance from salt marsh environment: soil salinity, soil water content, and soil organic matter content. Multispectral sensors were successful in quantifying soil salinity and soil water content but failed to model soil organic matter. The study also demonstrated the value of minimum noise fraction transformation and ensemble analysis techniques for marsh soil mapping. The results suggest that imaging spectroscopy based modeling is a promising tool to quantify and map marsh soil properties at a local scale, and is a potential alternative to traditional soil data acquisition to support carbon cycle research and the conservation and restoration of tidal marshes.

关键词： Salt marsh Soil properties Imaging spectroscopy Machine learning object-based modeling

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Hierarchical modeling of Complex Internet of Things Systems Using Conceptual modeling Approaches

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IEEE ACCESS 2019年 7卷 102772-102791页

作者： Abbasi, Khurrum Mustafa Khan, Tamim Ahmed Ul Haq, Irfan Bahria Univ Dept Software Engn Islamabad 44000 Pakistan PIEAS Dept CIS Islamabad 45650 Pakistan

Popularity of the Internet of things (IoT) is currently on the rise. Academia as well as industry is equally fascinated by this disruptive technology. The IoT research and implementation have succeeded securing their place in numerous domains including, but not limited to, business, transportation and logistics, health care, smart cities, and agriculture. Conceptual modeling is pivotal to the design and implementation of a system. Various modeling approaches have been used for a variety of the IoT applications and case studies. In this paper, we examine various innovative applications of the existing conceptual modeling approaches for the IoT. We analyze the use of these approaches against different concerns in isolation as well as in mutual combination for the IoT. We also define a set of terminologies that we frequently encounter in this manuscript and those likely to be used while adopting proposed modeling approaches for the IoT scenarios. This study also attempts to reconcile different modeling approaches toward a unified modeling system. For this purpose, we map various conceptual modeling approaches against different layers of functional model of the IoT reference architecture. There is also a scenario in the end of the paper which elaborates the use of different modeling approaches in hierarchy for a complex IoT system.

关键词： Agent-based modeling ambient-oriented modeling aspect-oriented modeling conceptual modeling approaches contract-based modeling fuzzy-logic modeling the Internet of Things network-based modeling object-based modeling ontology service-oriented modeling

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Reservoir modeling with publicly available software

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COMPUTERS & GEOSCIENCES 1999年第4期25卷 355-363页

作者： Deutsch, CV Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 2G7 Canada

Numerical reservoir models are extensively used to determine in-place hydrocarbon resources and recoverable reserves. Geostatistical techniques are being increasingly used to construct such numerical models. The second edition of the GSLIB software together with other publicly available software provides the tools needed to construct realistic models of large and complex reservoirs. This paper describes how to model lithofacies, porosity and permeability in sedimentary formations, particularly petroleum reservoirs, with publicly available software. Programs for postprocessing tasks such as scale up and management of multiple realizations are also presented. The relative merits of expensive commercial software are discussed. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： lithofacies modeling object-based modeling porosity permeability reservoir characterization scale up

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INTRODUCTION TO SIMIO

INTRODUCTION TO SIMIO

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2010 Winter Simulation Conference

作者： Pegden, C. Dennis Sturrock, David T. Simio LLC Sewickley PA 15143 USA

ISBN: (纸本)9781424498642

This paper describes the modeling system - Simio (TM)- that is designed to simplify model building by promoting a modeling paradigm shift from the process orientation to an object orientation. Simio is a (sim) under bar ulation modeling framework based on (i) under bar ntelligent (o) under bar bjects. The intelligent objects are built by modelers and then may be reused in multiple modeling projects. Although the Simio framework is focused on object-based modeling, it also supports a seamless use of multiple modeling paradigms including event, process, object, and agent-based modeling.

关键词： Simio agent-based modeling digital simulation intelligent objects modeling paradigm shift multi-agent systems object orientation object-based modeling process orientation simulation modeling framework

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Dynamic Behavior of Operating Crew in Complex Systems

Dynamic Behavior of Operating Crew in Complex Systems

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60th Annual Reliability and Maintainability Symposium (RAMS)

作者： Azarkhil, Mandana Mosleh, Ali Univ Maryland Dept Mech Engn Ctr Risk & Reliabil College Pk MD 20742 USA

ISBN: (纸本)9781479928477

Individuals working as a team introduce team related issues such as interpersonal coordination, supervision, time, and task management which are influenced by nontechnical, cognitive and social skills of team members. In control room of complex systems and under high task load situations, teams of operators are solely responsible for the ultimate decision making and control;but incomplete information, little time, stressful conditions and lack of mutual awareness can lead to critical human errors and the aggregate impact of such errors inside communication loops can give rise to complicated failure modes. This research develops a method to explicitly model the operating crew as an interactive social unit and investigates the dynamic behavior of the team under upset situations through a simulation method. The ultimate goal is to study the effects of team factors and team dynamics on the risk of the complex system with the main focus being on team errors, associated causes and error management inside the team and the impact of such factors on team performance. Information sharing, distribution, and collection, building of shared mental models, team decision making and combined action execution by the operating crew are being systematically modeled and examined. An object based modeling methodology is applied to represent system elements and different roles and behaviors of the members of the operating team. The proposed team model is an extended version of an existing cognitive model of individual operator behavior known as IDAC (Information, Decision, and Action in Crew context) (Chang et al, 2007). Scenario generation follows typical DPRA (Dynamic Probabilistic Risk Assessment) methodologies. The method capabilities are demonstrated through building and simulating a simplified model of a steam/power generating plant. Different configurations of team characteristics and influencing factors have been simulated and the performance is compared based on observing paramet

关键词： Human Reliability object-based modeling Simulation Dynamic PRA

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Image based simulation methods for depositional systems modeling

Image based simulation methods for depositional systems mode...

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作者： Viviana Lorena Vargas Grajales Pontificia Universidade Catolica do Rio de Janeiro

学位级别：博士

In this work, we present two geostatistical methods to model geological structures that exhibit directional features in a tree structure, like fan deltas and turbidite channels. The first method is a multiple point geostatistical algorithm called directional field-based simulation (DIR-SIM). The directional feature of the training image is used to create a new object that we call training directional field (TDF), which contains the direction in each point of the image. This TDF represents the training image in a broader sense because both the training image and the direction followed by the reservoir are contained there. We propose to apply this object as a fundamental tool in the simulation. The second method is an object- based simulation called SKE-SIM which uses a training image to extract the distribution of selected parameters to build the turbidite channel system. The idea is based on the premise that the training image can be well represented by a one-dimensional object that we call, skeleton.

关键词： Multiple-Point geostatistics Training image Directional field object-based modeling Skeleton

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Permeability Estimation In Turbidite Channels Constrained By Well-Testing

Permeability Estimation In Turbidite Channels Constrained By...

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作者： TAMIRES PEREIRA PINTO DA SILVA Pontifical Catholic University of Rio de Janeiro

学位级别：硕士

The main objective of this work is to populate turbidite channels with some petrophysical property such as permeability. These channels are geometrically constrained by turbidite lobes creating a simulated depositional system. Numerical simulations are used to try to fit the permeability field to a reference case through an objective function. A conventional finite difference simulator was used to compare the reference data to the simulations, obtaining close results.

关键词： Well testing Geological model object-based modeling Geostatistics Fluid-flow simulation

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