Addresses the burning questions in 3D modelling:
What is a good model?
What is its usability (beyond pretty pictures)?
How reproducible and extensible is it?
How can we separate data and interpretation?
How do we consider model uncertainty?
Features a Bayesian model space exploration of a synthetic case study
This document outlines the process for reservoir characterization, which involves multi-disciplinary analyses including: 1) geological analyses of core data, well logs, and cross sections; 2) analysis of geological databases; 3) evaluation of source rock and rock mechanics; 4) geophysical evaluation and interpretation of seismic data; and 5) reservoir engineering analyses including completion and drilling evaluations. The results of these analyses will be integrated into reservoir models to identify potential drilling locations with greater producibility and returns on investment.
Multi-legged Robot Walking Strategies, with an Emphasis on Image-based MethodsKazi Mostafa
The document outlines a research project on developing edge detection methods and walking strategies for multi-legged robots. It discusses using morphological operations on hexagonal grid images to remove noise and detect edges for low resolution images in real-time applications with low computational power. It describes developing structuring elements of various sizes and directions, and comparing performance of hexagonal versus rectangular grid images. The document also explores using fuzzy morphology and discusses evaluating different methods to determine the optimal approach for edge detection to enable efficient walking strategies for robots with damaged legs.
Stop Making Pie Charts (an opinionated guide to data visualisation)robingower
Don’t let Excel’s default settings ruin your data analysis! Learn insights from research into visual perception and interpretation. These slides present some great ideas stolen from the likes of Edward Tufte, Leland Wilkinson, and Stephen Few. You should be prepared never to look at a pie chart quite the same way again!
Feature based similarity search in 3 d object databasesunyil96
This document discusses feature-based similarity search in 3D object databases. It surveys existing feature-based methods for 3D object retrieval and proposes a taxonomy for these methods. The document also presents experimental results that compare the effectiveness of some surveyed methods. Similarity search in 3D object databases has applications in fields like computer-aided design, medicine, molecular biology, and military applications. Defining similarity among 3D objects and designing algorithms to implement such similarity definitions is a difficult problem that researchers have worked to solve.
This document discusses cluster analysis and clustering methods. It begins by defining cluster analysis and describing its goal of grouping similar data objects into clusters. It then categorizes major clustering methods into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods. Finally, it discusses calculating distances between data objects and clusters.
Build Your Own 3D Scanner:
Introduction
http://mesh.brown.edu/byo3d/
SIGGRAPH 2009 Courses
Douglas Lanman and Gabriel Taubin
This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.
This interdisciplinary tutorial was presented at the 2017 IEEE International Conference on Data Engineering in San Diego.
Reference:
Andreas Züfle, Goce Trajcevski, Dieter Pfoser, Matthew T. Rice, Matthias Renz, Timothy Leslie, Paul Delamater and Tobias Emrich. Handling Uncertainty in Geo-Spatial Data. 33rd International Conference on Data Engineering (ICDE). 2017.
This document outlines the process for reservoir characterization, which involves multi-disciplinary analyses including: 1) geological analyses of core data, well logs, and cross sections; 2) analysis of geological databases; 3) evaluation of source rock and rock mechanics; 4) geophysical evaluation and interpretation of seismic data; and 5) reservoir engineering analyses including completion and drilling evaluations. The results of these analyses will be integrated into reservoir models to identify potential drilling locations with greater producibility and returns on investment.
Multi-legged Robot Walking Strategies, with an Emphasis on Image-based MethodsKazi Mostafa
The document outlines a research project on developing edge detection methods and walking strategies for multi-legged robots. It discusses using morphological operations on hexagonal grid images to remove noise and detect edges for low resolution images in real-time applications with low computational power. It describes developing structuring elements of various sizes and directions, and comparing performance of hexagonal versus rectangular grid images. The document also explores using fuzzy morphology and discusses evaluating different methods to determine the optimal approach for edge detection to enable efficient walking strategies for robots with damaged legs.
Stop Making Pie Charts (an opinionated guide to data visualisation)robingower
Don’t let Excel’s default settings ruin your data analysis! Learn insights from research into visual perception and interpretation. These slides present some great ideas stolen from the likes of Edward Tufte, Leland Wilkinson, and Stephen Few. You should be prepared never to look at a pie chart quite the same way again!
Feature based similarity search in 3 d object databasesunyil96
This document discusses feature-based similarity search in 3D object databases. It surveys existing feature-based methods for 3D object retrieval and proposes a taxonomy for these methods. The document also presents experimental results that compare the effectiveness of some surveyed methods. Similarity search in 3D object databases has applications in fields like computer-aided design, medicine, molecular biology, and military applications. Defining similarity among 3D objects and designing algorithms to implement such similarity definitions is a difficult problem that researchers have worked to solve.
This document discusses cluster analysis and clustering methods. It begins by defining cluster analysis and describing its goal of grouping similar data objects into clusters. It then categorizes major clustering methods into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods. Finally, it discusses calculating distances between data objects and clusters.
Build Your Own 3D Scanner:
Introduction
http://mesh.brown.edu/byo3d/
SIGGRAPH 2009 Courses
Douglas Lanman and Gabriel Taubin
This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.
This interdisciplinary tutorial was presented at the 2017 IEEE International Conference on Data Engineering in San Diego.
Reference:
Andreas Züfle, Goce Trajcevski, Dieter Pfoser, Matthew T. Rice, Matthias Renz, Timothy Leslie, Paul Delamater and Tobias Emrich. Handling Uncertainty in Geo-Spatial Data. 33rd International Conference on Data Engineering (ICDE). 2017.
this's my presentation during 610 class.
idea like how i gonna create that 3D phenomena to explain those concepts
but im not gonna use it anymore ... after so many things had change
This document describes research into making digital elevation models (DEMs) more interactive. It discusses how most DEM display programs only show surface contours or parallel profiles, without allowing addition of other data or direct interaction. The researchers developed a new concept where visibility is stored as a property of spatial units (points, triangles), rather than computed only for display. This allows functions like adding roads/buildings, querying point locations, and rotating the surface without recomputing visibility each time. It explains how this concept was implemented in their triangular irregular network (TIN) DEM system to compute visibility in stages and store intermediate results in the database.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
It has been a longstanding challenge in geometric morphometrics and medical imaging to infer the physical locations (or regions) of 3D shapes that are most associated with a given response variable (e.g. class labels) without needing common predefined landmarks across the shapes, computing correspondence maps between the shapes, or requiring the shapes to be diffeomorphic to each other. In this talk, we introduce SINATRA: the first statistical pipeline for sub-image analysis which identifies physical shape features that explain most of the variation between two classes without the aforementioned requirements. We also illustrate how the problem of 3D sub-image analysis can be mapped onto the well-studied problem of variable selection in nonlinear regression models. Here, the key insight is that tools from integral geometry and differential topology, specifically the Euler characteristic, can be used to transform a 3D mesh representation of an image or shape into a collection of vectors with minimal loss of geometric information.
Crucially, this transform is invertible. The two central statistical, computational, and mathematical innovations of our method are: (1) how to perform robust variable selection in the transformed space of vectors, and (2) how to pullback the most informative features in the transformed space to physical locations or regions on the original shapes. We highlight the utility, power, and properties of our method through detailed simulation studies, which themselves are a novel contribution to 3D image analysis. Finally, we apply SINATRA to a dataset of mandibular molars from four different genera of primates and demonstrate the ability to identify unique morphological properties that summarize phylogeny.
The Inquisitive Data Scientist: Facilitating Well-Informed Data Science throu...Cagatay Turkay
Slides for my talk at the VRVis Research Centre in Vienna as part of their VRVIS Forum talk series on November 8th 2018 -- https://www.vrvis.at/newsroom/events/forum/148-invited-talk-by-cagatay-turkay-the-inquisitive-data-scientist/
The talk argues the importance of being "inquisitive" as a data scientist and discusses techniques from visualisation that support this.
A Framework for Desktop Virtual Reality Application for EducationMangaiK4
Abstract: Contemporary custom in education encourages students to gain exposure in the real world through student visits (field visits) to sites in count to conventional textbooks and lectures. This disclosure helps students to experience real world situations and integrate this experience into knowledge learned in class. This is important to students in various disciplines such as engineering, architecture and transportation. Students, however, have limited on-site access due to issues related to safety concerns, cost and effort. In an attempt to address such issues, Virtual Reality (VR) applications have been developed and implemented. With the growth in the number of VR applications, there is currently a lack of information about the design issues of VR applications from the standpoint of integrating different types of information associated with the real world. This paper aims to bridge this gap by evaluating VR applications with respect to these issues and highlights the lessons learned from the appropriate evaluations. The results demonstrate that VR application, which links different sources of information (as developed in this paper), promotes better learning than conventional printed materials and that students professed it positively as a precious complement to a physical field visit. The design recommendations for the development of similar VR learning applications are further discussed in this paper.
A Framework for Desktop Virtual Reality Application for EducationMangaiK4
This document discusses the development of a virtual reality (VR) application for educational purposes. It begins by outlining the benefits of field visits for students but also notes the limitations of physical access. To address this, the document proposes a desktop VR application that can integrate different sources of information about the real world. It reviews literature on educational VR applications and design principles. The proposed application aims to link various types of information within a single screen to promote better learning compared to conventional materials. The document discusses evaluating the application for usability and learning effectiveness. Overall, the VR application is intended to provide students with an interactive virtual field visit experience while integrating multiple information sources for enhanced learning.
C documents and settings_administrator_local settings_application data_mozil...Anuar Ahmad
This document discusses digital terrain modeling for GIS applications. It begins by introducing digital terrain models (DTMs) and their importance for applications like flood analysis. It then discusses two common methods for creating DTMs - interpolation of terrain points and using photogrammetry. The main body focuses on assessing the quality of DTMs, describing statistical and visual quality assessment techniques. It applies several interpolation algorithms to real terrain data from Oradea, Romania to compare results. Statistical analysis showed the Delaunay triangulation and Shepard's interpolation methods produced the most accurate models. The document concludes higher point densities improve DTM quality and Delaunay triangulation is recommended when densities allow at least 3 points per terrain feature.
This document summarizes a technical conference paper about assessing the quality of digital terrain models (DTMs). It discusses how DTMs are used in geographic information systems and engineering applications. The paper presents several visual techniques for evaluating DTM quality, including comparing interpolation methods used to generate DTMs from known elevation points. Models created using Delaunay triangulation and Shepard interpolation were found to best match a reference DTM for test areas in Oradea, Romania. The document concludes visual analysis and statistical parameters are effective for comparing DTM quality.
The document discusses chapter 8 of a textbook on data mining concepts and techniques. It covers various topics related to cluster analysis, including what cluster analysis is, different types of data that can be used for cluster analysis, major categories of clustering methods like partitioning, hierarchical, density-based, grid-based, and model-based methods. It also discusses outlier analysis and provides examples of clustering applications.
This document describes research on using mobile hyperspectral imaging to detect material surface damage. It discusses how hyperspectral imaging captures hundreds of spectral reflectance values per pixel, providing more information than traditional RGB images. The researchers developed a mobile hyperspectral imaging system and machine learning models to classify different surface objects, including cracks. Experimental results showed hyperspectral pixels could identify eight surface objects with high accuracy, outperforming gray-valued images with higher spatial resolution. The researchers conclude hyperspectral imaging has great potential for automating damage inspection, especially for complex scenes on built structures.
PLOTCON NYC: Custom Colormaps for Your FieldPlotly
Visualizations can be clear or obscure depending on the color scheme used to represent the data, and careful use of color can also be attractive. However, colormaps have not generally received the attention they deserve, given their significance. The colors used carry the responsibility of conveying data honestly and accurately. They should generally be perceptually uniform so that equal steps through the dataset are represented by equal perceptual jumps in the colormap. They should be intuitive to help support quick, natural understanding of the data. They should match basic properties of the data, like showing the presence of information (sequential) or anomalies in a field (diverging). Additionally, just as different variables are typically represented with different specific Greek letters when written, different variables should also be represented with different colormaps when plotted. A suite of colormaps called cmocean have been developed to meet the needs of oceanographers, and can be used by any plotter out there. The suite is freely available for many different software packages (including Python and R). You can use these colormaps to help convey your data honestly and accurately.
The document summarizes a two-year project that used internet-based activities to help first-year geoscience students develop their understanding of 3D spatial relationships. The project tested students' spatial skills before and after using the online activities. It provided over 40 activities covering topics like minerals, fossils, volcanoes, and maps. Student feedback on the activities was positive and showed improved spatial awareness after using the resources. The project aims to help students who struggle with visualizing 3D relationships, which is an important skill for geoscience disciplines.
Don’t let Excel’s default settings ruin your data analysis! Learn insights from research into visual perception and interpretation. Robin Gower will present some great ideas stolen from the likes of Edward Tufte, Leland Wilkinson, and Stephen Few. You don’t need to be a technical user to enjoy the talk but you should be prepared never to look at a pie chart quite the same way again!
Robin is a freelance data engineer http://infonomics.ltd.uk/ and long-term mitherer at ODM
This document discusses approaches for video segmentation. It describes tracking particles across frames to identify motion patterns, then clustering the particles to obtain a pixel-wise segmentation over space and time. This addresses limitations of segmentation based on motion boundaries. Reality-based 3D models can help address complex spatial motions by representing objects and their relationships in 3D space. The document also reviews direct and feature-based motion estimation methods, variational and level-set segmentation frameworks, and challenges including fitting motion models to data and handling outliers.
This document summarizes a study that aimed to identify the main factors that explain resistance to corporate distance education in a military institution. The researchers developed a theoretical model called READEC and tested it using a survey of 230 military personnel taking an online course. The results provided partial support for READEC, finding that self-efficacy and performance expectations positively influenced resistance as hypothesized, but that facilitating conditions did not. This refinement of READEC improved the model's ability to explain resistance to distance education. The study concluded that resistance is better understood by considering an individual's confidence in their ability to learn independently and their expectations of how well a distance course will help them perform.
John McGaughey, CEO/President of Mira Geoscience offers his thoughts and the practices of integrated geophysical interpretation at the 3D Interest Group
This document discusses a study that examines the genetic basis of mouse mandible shape using 3D phenotyping and landmarks. The study aims to validate and improve upon previous QTL mapping studies of mouse mandible shape by applying 3D micro-CT imaging, 3D landmarks, and geometric morphometrics. The study compares results using different landmark configurations, including 2D versus 3D landmarks and manual versus semilandmarks. The study finds that using a large set of semilandmarks coupled with manual landmarks identifies significantly more QTLs and maps them more precisely, suggesting finer phenotypic characterization with 3D landmarks yields better insights into mandibular genetic architecture. However, most variation is still embedded in the natural 2D plane of the
This chapter discusses research design in marketing research. It defines research design and describes the main types: exploratory, descriptive, and causal research. Exploratory research is used to define problems or generate hypotheses, descriptive research describes characteristics of populations or behaviors, and causal research determines causes and effects. The chapter outlines the tasks involved in research design and compares different design methods and approaches.
Lutz Gross of the University of Queensland describes running geophysical inversion using e-script, an open source package based on PDEs and python. Other examples of what e-script can do are also shown, such as diffusion calculations, mantle convection, flow in porous media, seismo-electrics and much more!
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this's my presentation during 610 class.
idea like how i gonna create that 3D phenomena to explain those concepts
but im not gonna use it anymore ... after so many things had change
This document describes research into making digital elevation models (DEMs) more interactive. It discusses how most DEM display programs only show surface contours or parallel profiles, without allowing addition of other data or direct interaction. The researchers developed a new concept where visibility is stored as a property of spatial units (points, triangles), rather than computed only for display. This allows functions like adding roads/buildings, querying point locations, and rotating the surface without recomputing visibility each time. It explains how this concept was implemented in their triangular irregular network (TIN) DEM system to compute visibility in stages and store intermediate results in the database.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
It has been a longstanding challenge in geometric morphometrics and medical imaging to infer the physical locations (or regions) of 3D shapes that are most associated with a given response variable (e.g. class labels) without needing common predefined landmarks across the shapes, computing correspondence maps between the shapes, or requiring the shapes to be diffeomorphic to each other. In this talk, we introduce SINATRA: the first statistical pipeline for sub-image analysis which identifies physical shape features that explain most of the variation between two classes without the aforementioned requirements. We also illustrate how the problem of 3D sub-image analysis can be mapped onto the well-studied problem of variable selection in nonlinear regression models. Here, the key insight is that tools from integral geometry and differential topology, specifically the Euler characteristic, can be used to transform a 3D mesh representation of an image or shape into a collection of vectors with minimal loss of geometric information.
Crucially, this transform is invertible. The two central statistical, computational, and mathematical innovations of our method are: (1) how to perform robust variable selection in the transformed space of vectors, and (2) how to pullback the most informative features in the transformed space to physical locations or regions on the original shapes. We highlight the utility, power, and properties of our method through detailed simulation studies, which themselves are a novel contribution to 3D image analysis. Finally, we apply SINATRA to a dataset of mandibular molars from four different genera of primates and demonstrate the ability to identify unique morphological properties that summarize phylogeny.
The Inquisitive Data Scientist: Facilitating Well-Informed Data Science throu...Cagatay Turkay
Slides for my talk at the VRVis Research Centre in Vienna as part of their VRVIS Forum talk series on November 8th 2018 -- https://www.vrvis.at/newsroom/events/forum/148-invited-talk-by-cagatay-turkay-the-inquisitive-data-scientist/
The talk argues the importance of being "inquisitive" as a data scientist and discusses techniques from visualisation that support this.
A Framework for Desktop Virtual Reality Application for EducationMangaiK4
Abstract: Contemporary custom in education encourages students to gain exposure in the real world through student visits (field visits) to sites in count to conventional textbooks and lectures. This disclosure helps students to experience real world situations and integrate this experience into knowledge learned in class. This is important to students in various disciplines such as engineering, architecture and transportation. Students, however, have limited on-site access due to issues related to safety concerns, cost and effort. In an attempt to address such issues, Virtual Reality (VR) applications have been developed and implemented. With the growth in the number of VR applications, there is currently a lack of information about the design issues of VR applications from the standpoint of integrating different types of information associated with the real world. This paper aims to bridge this gap by evaluating VR applications with respect to these issues and highlights the lessons learned from the appropriate evaluations. The results demonstrate that VR application, which links different sources of information (as developed in this paper), promotes better learning than conventional printed materials and that students professed it positively as a precious complement to a physical field visit. The design recommendations for the development of similar VR learning applications are further discussed in this paper.
A Framework for Desktop Virtual Reality Application for EducationMangaiK4
This document discusses the development of a virtual reality (VR) application for educational purposes. It begins by outlining the benefits of field visits for students but also notes the limitations of physical access. To address this, the document proposes a desktop VR application that can integrate different sources of information about the real world. It reviews literature on educational VR applications and design principles. The proposed application aims to link various types of information within a single screen to promote better learning compared to conventional materials. The document discusses evaluating the application for usability and learning effectiveness. Overall, the VR application is intended to provide students with an interactive virtual field visit experience while integrating multiple information sources for enhanced learning.
C documents and settings_administrator_local settings_application data_mozil...Anuar Ahmad
This document discusses digital terrain modeling for GIS applications. It begins by introducing digital terrain models (DTMs) and their importance for applications like flood analysis. It then discusses two common methods for creating DTMs - interpolation of terrain points and using photogrammetry. The main body focuses on assessing the quality of DTMs, describing statistical and visual quality assessment techniques. It applies several interpolation algorithms to real terrain data from Oradea, Romania to compare results. Statistical analysis showed the Delaunay triangulation and Shepard's interpolation methods produced the most accurate models. The document concludes higher point densities improve DTM quality and Delaunay triangulation is recommended when densities allow at least 3 points per terrain feature.
This document summarizes a technical conference paper about assessing the quality of digital terrain models (DTMs). It discusses how DTMs are used in geographic information systems and engineering applications. The paper presents several visual techniques for evaluating DTM quality, including comparing interpolation methods used to generate DTMs from known elevation points. Models created using Delaunay triangulation and Shepard interpolation were found to best match a reference DTM for test areas in Oradea, Romania. The document concludes visual analysis and statistical parameters are effective for comparing DTM quality.
The document discusses chapter 8 of a textbook on data mining concepts and techniques. It covers various topics related to cluster analysis, including what cluster analysis is, different types of data that can be used for cluster analysis, major categories of clustering methods like partitioning, hierarchical, density-based, grid-based, and model-based methods. It also discusses outlier analysis and provides examples of clustering applications.
This document describes research on using mobile hyperspectral imaging to detect material surface damage. It discusses how hyperspectral imaging captures hundreds of spectral reflectance values per pixel, providing more information than traditional RGB images. The researchers developed a mobile hyperspectral imaging system and machine learning models to classify different surface objects, including cracks. Experimental results showed hyperspectral pixels could identify eight surface objects with high accuracy, outperforming gray-valued images with higher spatial resolution. The researchers conclude hyperspectral imaging has great potential for automating damage inspection, especially for complex scenes on built structures.
PLOTCON NYC: Custom Colormaps for Your FieldPlotly
Visualizations can be clear or obscure depending on the color scheme used to represent the data, and careful use of color can also be attractive. However, colormaps have not generally received the attention they deserve, given their significance. The colors used carry the responsibility of conveying data honestly and accurately. They should generally be perceptually uniform so that equal steps through the dataset are represented by equal perceptual jumps in the colormap. They should be intuitive to help support quick, natural understanding of the data. They should match basic properties of the data, like showing the presence of information (sequential) or anomalies in a field (diverging). Additionally, just as different variables are typically represented with different specific Greek letters when written, different variables should also be represented with different colormaps when plotted. A suite of colormaps called cmocean have been developed to meet the needs of oceanographers, and can be used by any plotter out there. The suite is freely available for many different software packages (including Python and R). You can use these colormaps to help convey your data honestly and accurately.
The document summarizes a two-year project that used internet-based activities to help first-year geoscience students develop their understanding of 3D spatial relationships. The project tested students' spatial skills before and after using the online activities. It provided over 40 activities covering topics like minerals, fossils, volcanoes, and maps. Student feedback on the activities was positive and showed improved spatial awareness after using the resources. The project aims to help students who struggle with visualizing 3D relationships, which is an important skill for geoscience disciplines.
Don’t let Excel’s default settings ruin your data analysis! Learn insights from research into visual perception and interpretation. Robin Gower will present some great ideas stolen from the likes of Edward Tufte, Leland Wilkinson, and Stephen Few. You don’t need to be a technical user to enjoy the talk but you should be prepared never to look at a pie chart quite the same way again!
Robin is a freelance data engineer http://infonomics.ltd.uk/ and long-term mitherer at ODM
This document discusses approaches for video segmentation. It describes tracking particles across frames to identify motion patterns, then clustering the particles to obtain a pixel-wise segmentation over space and time. This addresses limitations of segmentation based on motion boundaries. Reality-based 3D models can help address complex spatial motions by representing objects and their relationships in 3D space. The document also reviews direct and feature-based motion estimation methods, variational and level-set segmentation frameworks, and challenges including fitting motion models to data and handling outliers.
This document summarizes a study that aimed to identify the main factors that explain resistance to corporate distance education in a military institution. The researchers developed a theoretical model called READEC and tested it using a survey of 230 military personnel taking an online course. The results provided partial support for READEC, finding that self-efficacy and performance expectations positively influenced resistance as hypothesized, but that facilitating conditions did not. This refinement of READEC improved the model's ability to explain resistance to distance education. The study concluded that resistance is better understood by considering an individual's confidence in their ability to learn independently and their expectations of how well a distance course will help them perform.
John McGaughey, CEO/President of Mira Geoscience offers his thoughts and the practices of integrated geophysical interpretation at the 3D Interest Group
This document discusses a study that examines the genetic basis of mouse mandible shape using 3D phenotyping and landmarks. The study aims to validate and improve upon previous QTL mapping studies of mouse mandible shape by applying 3D micro-CT imaging, 3D landmarks, and geometric morphometrics. The study compares results using different landmark configurations, including 2D versus 3D landmarks and manual versus semilandmarks. The study finds that using a large set of semilandmarks coupled with manual landmarks identifies significantly more QTLs and maps them more precisely, suggesting finer phenotypic characterization with 3D landmarks yields better insights into mandibular genetic architecture. However, most variation is still embedded in the natural 2D plane of the
This chapter discusses research design in marketing research. It defines research design and describes the main types: exploratory, descriptive, and causal research. Exploratory research is used to define problems or generate hypotheses, descriptive research describes characteristics of populations or behaviors, and causal research determines causes and effects. The chapter outlines the tasks involved in research design and compares different design methods and approaches.
Similar to Florian Wellmann: Uncertainties in 3D Models (20)
Lutz Gross of the University of Queensland describes running geophysical inversion using e-script, an open source package based on PDEs and python. Other examples of what e-script can do are also shown, such as diffusion calculations, mantle convection, flow in porous media, seismo-electrics and much more!
This talk describes the process of generating a 3D model of the Kevitsa (Finland) ore body through wavelet transform of geochemistry obtained from drill core. Tesselation is then used to determine an appropriate scale of study for the data and 3D modelling. Subtle signals are identified, while the effects of analytical noise are dampened through this process. A genetic model for ore body formation was also formulated due to the success of the data filtering process.
Drawing on hands-on experience and theoretical contributions Serge will encourage attendees to consider innovative approaches to problems across the mining logic chain, with examples including:
• Porphyry unit modelling - Simulations
• Integrating grade control and resource drilling data – Co-kriging
• Modelling geotechnical characteristics - Directional Concentration
• Predicting metallurgical recovery & sampling – non additivity
Mark Jessell from the Centre for Exploration Targeting at the University of Western Australia presents his latest work on using geological relationships to improve our 3D modelling and mineral systems analyses.
Jeremie Giraud's PhD research being conducted at the Centre for Exploration Targeting, University of Western Australia is investigating the use of probabilistic geological models and statistical distributions of petrophysics to constrain joint potential field inversion.
Prof. David Lumley from the Centre of Energy Geoscience at the Uni. Of Western Australia presents his work on “Nonlinear Uncertainty Analysis: 4D Seismic reservoir monitoring”.
This document outlines the geostatistical workflow for modeling spatial variability in mineral properties. The workflow involves collecting real data, modeling the spatial variability, generating multiple realizations of the properties, and selecting realizations that produce reasonable results. It notes challenges in capturing real spatial variability and ensuring consistency across all modeled properties.
This presentation was presented by Florian Wellmann, Mark Lindsay and Mark Jessell and the recent EGU 2015 conference.
____
Geological models are widely used to represent the structural setting of the subsurface. Commonly, a single model is generated for a region, representing the best interpretation of the structural setting in the light of all available information. It is, however, widely accepted that a such created model still contains uncertainties. We hypothesise here that it is possible to transform a single kinematic model into a powerful predictive tool for scenario analysis and uncertainty quantification.
We extend the functionality of a kinematic structural and geophysical modelling approach, implemented in the software Noddy, with a set newly developed Python modules to expose, generalise and automate essential
parts of the modelling workflow. We show how these methods enable us to quickly generate and analyse different geological scenarios.
In addition to the geological model, Noddy also enables the direct calculation of geophysical fields of gravity and magnetics. We can use this functionality to compare the model to measured potential fields. With an example for a fold and thrust belt model, we show how to quickly estimate how changes in the model (due to parameter uncertainties, for example) affect the calculated gravity field in the model range.
Finally, we present the possibility to efficiently generate an ensemble of model realisations for predictive geomodel analysis with an application to a case study in the Gippsland Basin, Victoria. The results show that our
approach can successfully extend the functionality of traditional modelling methods with an additional layer of
predictive power towards an efficient evaluation of uncertainties in structural geological models.
The Geological Survey of Western Australia is developing 3D modeling capabilities to better integrate geological, geophysical, and geochemical data and increase knowledge of Western Australia's subsurface. A team is building and managing 3D structural models according to standards and stakeholder needs while collaborating with research institutions. Challenges include integrating diverse existing data, producing useful products, developing technical frameworks, and managing large volumes of data and models. The GSWA is focusing on regional 3D models of areas like the Yilgarn Craton and its margins through projects involving seismic surveys, gigapixel images, and physical property modeling. The goal is to provide updated digital 3D model packages of Western Australia annually.
PICO presentation at EGU 2014 about the use of measures from information theory to visualise uncertainty in kinematic structural models - and to estimate where additional data would help reduce uncertainties. Some nice counter-intuitive results ;-)
Developing better integration of geological constraints into 3D regional modelling
Identify ways to carry geological meaning through the geophysical inversion process
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You may be stressed about revealing your cancer diagnosis to your child or children.
Children love stories and these often provide parents with a means of broaching tricky subjects and so the ‘The Secret Warrior’ book was especially written for CANSA TLC, by creative writer and social worker, Sally Ann Carter.
Find out more:
https://cansa.org.za/resources-to-help-share-a-parent-or-loved-ones-cancer-diagnosis-with-a-child/
ProSocial Behaviour - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Covey says most people look for quick fixes. They see a big success and want to know how he did it, believing (and hoping) they can do the same following a quick bullet list.
But real change, the author says, comes not from the outside in, but from the inside out. And the most fundamental way of changing yourself is through a paradigm shift.
That paradigm shift is a new way of looking at the world. The 7 Habits of Highly Effective People presents an approach to effectiveness based on character and principles.
The first three habits indeed deal with yourself because it all starts with you. The first three habits move you from dependence from the world to the independence of making your own world.
Habits 4, 5 and 6 are about people and relationships. The will move you from independence to interdependence. Such, cooperating to achieve more than you could have by yourself.
The last habit, habit number 7, focuses on continuous growth and improvement.
Understanding of Self - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Aggression - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
1. Uncertainties in 3-D Structural Models
A Probabilistic Perspective and some Considerations to include Additional
Geological Knowledge
Centre for Exploration Targeting (CET)
Geomodelling seminar presentation
March 1, 2014
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 1 / 55
2. Overview of Presentation
3-D Geological
Modelling
Uncertainties
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55
3. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Model validation and
geological “rules”
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55
4. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55
5. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55
6. Overview of Presentation
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 2 / 55
7. Part 1: Geological Modelling and Uncertainties
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 3 / 55
8. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55
9. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55
10. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55
11. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
As basis for simulations
(property distributions)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55
12. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
As basis for simulations
(property distributions)
Prospectivity analysis
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55
13. Why 3-D Modelling?
Why make 3-D models?
To spin them around and
impress (“cyber-kinetic
art”)
As an act of learning
while modelling
3-D extension of maps
As basis for simulations
(property distributions)
Prospectivity analysis
Multiple methods and approaches
SKUA%
Earthvision% Geomodeller%
Noddy%
Explicit(
Implicit(
Kinema/c/(
Mechanical(
Geophysical(
Inversion(
VPmg%
Kine3D%
Vulcan%(old)%
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 4 / 55
14. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55
15. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55
16. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55
17. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
Separation of data and interpretation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55
18. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
Separation of data and interpretation
Consideration of uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55
19. Challenges in 3-D Modelling
Challenges depend on the application and the specific scale,
some general points:
What is a good model?
Usability (beyond pretty pictures)
Reproducibility and extensibility
Separation of data and interpretation
Consideration of uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 5 / 55
20. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Bardossy and Fodor (2001):
Sampling and
observation error
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55
21. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Inherent randomness
Bardossy and Fodor (2001):
Sampling and
observation error
Variability and
propagation error
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55
22. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Inherent randomness
Incomplete knowledge
Bardossy and Fodor (2001):
Sampling and
observation error
Variability and
propagation error
Conceptual and model
uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55
23. Uncertainties in 3-D Geological Modelling
Types of uncertainty
Mann (1993):
Error, bias, imprecision
Inherent randomness
Incomplete knowledge
Bardossy and Fodor (2001):
Sampling and
observation error
Variability and
propagation error
Conceptual and model
uncertainty
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 6 / 55
24. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 7 / 55
25. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 8 / 55
26. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 9 / 55
27. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 10 / 55
28. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 11 / 55
29. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 12 / 55
30. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 13 / 55
31. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 14 / 55
32. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 15 / 55
33. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 16 / 55
34. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 17 / 55
35. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 18 / 55
36. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 19 / 55
37. Geological Uncertainties are real
Field example by Courrioux et al.: comparing multiple 3-D models,
created for same region, by different teams of students
Unfortunately, quite infeasible in real applications...
Yellow lines: surface contacts White lines: faults
(From: Courrioux et al., 34th IGC, Brisbane, 2012)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 19 / 55
38. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55
39. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
Assign probability
distributions to observations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55
40. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
Assign probability
distributions to observations
Randomly generate new
observation sets
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55
41. Stochastic Geological Modelling
Stochastic modelling approach
Primary Observations
Realisation 1
Realisation n
Realisation 3
Realisation 2
Model 1
Model n
Model 3
Model 2
c
ologies per voxel 6
(Jessell et al., submitted)
Start with primary
observations
Assign probability
distributions to observations
Randomly generate new
observation sets
Create models for all sets
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 20 / 55
42. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55
43. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
How to ensure that models are valid?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55
44. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
How to ensure that models are valid?
How to include additional geological constraints and knowledge?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55
45. Analysis and visualisation
Analysis and visualisation of uncertainties
Realisation n
Realisation 3
Model n
Model 3
b
d e
c
PrincipalComponent2
Principal Component 1
0
0
0.40.30.2
0.4
-0.1-0.2-0.3-0.4
-0.3
-0.4
-0.2
-0.1
0.1
0.3
0.2
0.1
0.5
-0.5
0.5-0.5
Initial
model
Model space
boundary
2 Lithologies per voxel 6
Gravity misfit
-2.5 mgal 1.5
Figure 2
(Jessell et al., submitted)
Stochastic modelling works, but important further questions:
How to best analyse and visualise uncertainties?
How to ensure that models are valid?
How to include additional geological constraints and knowledge?
How to combine stochastic geological modelling with geophysical
inversions?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 21 / 55
46. Part 2: Model Validation and Geological “Rules”
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 22 / 55
47. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55
48. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55
49. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55
50. Geological rules and model validation
Problem outline
1 2 3
?
Initial model and input points and
their uncertainties
Reasonable model realisation Failure of model construction Failure of geological constraint
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 23 / 55
51. Simple model: Graben
Model of a simple graben (essentially 2-D)
1 km
1 km
Interpolation with Geomodeller,
automation with Python; 3-D view
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55
52. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55
53. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55
54. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
Uncertainties assigned to points as
normal distributions:
Faults: σ = 100 m in EW
direction
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55
55. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
Uncertainties assigned to points as
normal distributions:
Faults: σ = 100 m in EW
direction
Surfaces: σ = 75 m in z
direction
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55
56. Simple model: Graben
Model of a simple graben (essentially 2-D)
Geological parameters:
fault positions (•)
surface contact points (•)
Uncertainties assigned to points as
normal distributions:
Faults: σ = 100 m in EW
direction
Surfaces: σ = 75 m in z
direction
Geological knowledge: graben,
normal faulting, three layers
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 24 / 55
57. Model realisations - all models
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 25 / 55
58. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55
59. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
Regional thickness continuation
and variation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55
60. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
Regional thickness continuation
and variation
Combined effect of syntectonic
sedimentation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55
61. Consideration of geological knowledge
Encapsulating geological knowledge not taken into account by
the model interpolation method
Fault offset
Regional thickness continuation
and variation
Combined effect of syntectonic
sedimentation
Implementation of rules in Python package wrapping stochastic
geological uncertainty simulation and rejection sampling
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 26 / 55
62. Additional constraints
Additional constraints for Graben model
max
min
Additional constraints:
Min/max values for objects
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55
63. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55
64. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
Fault offset
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55
65. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
Fault offset
Thickness variation across
fault compartments
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55
66. Additional constraints
Additional constraints for Graben model
Additional constraints:
Min/max values for objects
Layer thickness
Fault offset
Thickness variation across
fault compartments
In total: 27 constraints based on
these geometric relationships defined.
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 27 / 55
67. Model realisations - validated models only
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 28 / 55
68. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55
69. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55
70. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
However:
Constraints are fixed values, whereas they might actually be highly
uncertain themselves!
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55
71. Conclusion
Conclusion from model validation step
First results show that automatic model validation step with additional
constraints is feasible
However:
Constraints are fixed values, whereas they might actually be highly
uncertain themselves!
Inefficient sampling, high rejection rate (> 99% in this case!)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 29 / 55
72. Part 3: Probabilistic Framework for Multiple Constraints
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 30 / 55
73. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
74. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
75. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
76. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
77. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
Additional theoretical considerations:
Efficiency of algorithm
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
78. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
Additional theoretical considerations:
Efficiency of algorithm
Possibility to explore wide range of parameter space (non-linearities)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
79. Probabilistic framework - concept
Idea
A flexible method is required to handle multiple, possibly
uncertain, additional constraints
Interesting scientific questions:
Which rules led to rejections?
Which parameter values led to valid models?
How are these parameters correlated?
Additional theoretical considerations:
Efficiency of algorithm
Possibility to explore wide range of parameter space (non-linearities)
Hypothesis: probabilistic Bayesian framework and combination with
Markov Chain Monte Carlo (MCMC) sampling suitable to address these
questions.
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 31 / 55
80. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55
81. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55
82. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
The geological rules are the (additional) data, p(y)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55
83. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
The geological rules are the (additional) data, p(y)
We want to know the posterior p(θ|y): probability (uncertainty) of a
geological parameter set, given geological rules
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55
84. Interpretation in the context of Geological Modelling
Bayes’ Rule – linking posterior through prior and likelihood
p(θ|y) =
p(y|θ)p(θ)
p(y)
(1)
We want to know how geological knowledge (“rules”) reduces the
uncertainty of the geological model, therefore:
The (uncertain) geological data are the model, p(θ)
The geological rules are the (additional) data, p(y)
We want to know the posterior p(θ|y): probability (uncertainty) of a
geological parameter set, given geological rules
We need to define the likelihood functions p(y|θ): probability of a
rule, given geological data set
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 32 / 55
85. Simple example
From simple graben to even simpler example
Reduce the simple graben model to its bare minimum:
From 3-D...
(which is essentially 2-
D)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 33 / 55
86. Simple example
From simple graben to even simpler example
Reduce the simple graben model to its bare minimum:
From 3-D...
(which is essentially 2-
D)
Depth
Some random x-range
Thickness (t1)
Depth of surface 1 (d1)
Depth of surface 2 (d2)
From 3-D (which is essentially 2-D) to 2-D (which is actually even 1-D...)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 33 / 55
93. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
94. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
95. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
96. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
97. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
98. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
Implement additional constraints (e.g. off-surface observations)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
99. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
Implement additional constraints (e.g. off-surface observations)
Detailed analysis of posterior distribution using information theory
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
100. Conclusion from probabilistic approach
What does posterior distribution tell us?
Valid range of model results
Parameter uncertainty reduction!
Insights into parameter correlations
Next steps for probabilistic framework
Use Markov Chain Monte Carlo sampling (with pymc) instead of
rejection algorithm (and compare efficiency)
Implement additional constraints (e.g. off-surface observations)
Detailed analysis of posterior distribution using information theory
Possibly analyse as Bayesian network
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 38 / 55
101. Part 3: Application: North Perth Basin
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 39 / 55
102. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55
103. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
Based on previous GSWA studies and
legacy data
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55
104. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
Based on previous GSWA studies and
legacy data
Significant uncertainties at depth
“...owing to the poor quality of
seismic data [...] [the top] Permian
is commonly only a phantom
horizon.” (Mory and Iasky, 1996)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55
105. Application to North Perth Basin
North Perth Basin probabilistic model – work in progress!
Regional scale model as basis for
geothermal resource estimations
Based on previous GSWA studies and
legacy data
Significant uncertainties at depth
“...owing to the poor quality of
seismic data [...] [the top] Permian
is commonly only a phantom
horizon.” (Mory and Iasky, 1996)
How uncertain is the model and how can additional information and
geological knowledge reduce these uncertainties?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 40 / 55
106. Model setup
Initial 3-D geological model
(Mory and Iasky, 1996)
Depth(km)
0
2
4
6
Extent: 34 km EW, 38 km NS, Depth to 7.5 km
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 41 / 55
107. Model setup
Initial 3-D geological model
(Mory and Iasky, 1996)
Depth(km)
0
2
4
6
Extent: 34 km EW, 38 km NS, Depth to 7.5 km
Interpolation with Geomodeller,
input data discretised as:
Surface contact points
Orientation measurements
Plus: definition of stratigraphy
and fault interaction
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 41 / 55
108. Uncertainties and constraints in cross-sections
Contact points in cross-sections and definition of fault compartments
Cross Section C
Cross Section B
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 42 / 55
(Jonathan Poh et al. in prep.)
109. Uncertainties and constraints in cross-sections
Contact points in cross-sections and definition of fault compartments
Cross Section C
Cross Section B
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
Depth(km)
0
5
Fault compartments
1
2
3
4
5
6
34 km
38 km
7.5 km
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 42 / 55
(Jonathan Poh et al. in prep.)
110. From tectonic and sedimentary evolution to geological rules
Sedimentary
Low High
1
3
4
5
6
Tectonics
Low High
Permian
EarlyLate
Triassic
EarlyLateMid
Jurassic
EarlyLateMid
Cretaceous
EarlyLate
1
2
3
4
5
6
7
8
9
10 7
2
Breakupof
Gondwana
Geological Evolution Combination Applicable Rules Fault Offset Result
Multiple cycles of syn-tectonic sedimentary deposition with
a decrease in effect from sedimentary processes
(Early Permian sequence)
Syn-depositional tectonics with a strong normal faulting
component and a gradually increasing sedimentary process
(Late Permian Sequence)
Syn-depositional tectonics with a decrease in tectonic strength
(reverse faulting took place), sedimentary processes is
assumed to be stablised (Kockatea Shale)
Syn-sedimentary tectonics with a low tectonic strength
(reverted to normal faulting), sedimentary processes have
stablised (Woodada Formation)
Syn-tectonic sedimentary with an slight increased strength
from minor fault event (Eneabba Formation)
Normal Fault + Sedimentary + Normal Fault
(Cattamarra Coal Formation)
Inferred weak sedimentary and tectonic sedimentary
(Cadda Formation)
Syn-sedimentary tectonics with inferred strong sedimentary
and regional tectonic forces (Yarragadee Formation)
Synchronous Rule II (a)
Synchronous Rule II (b)
Synchronous Rule III (b)
Synchronous Rule I, IV or
even sedimentary deposition
Synchronous Rule I
Discrete Rule VI
Synchronous Rule I
Synchronous Rule I
(with litho-stratigraphic unit)
Fault offset becomes more
pronounced
Fault offset has increased and
should be greater than the fault
offset during the Early Permian
Fault offset has decreased
Fault offset has increased
Fault offset has increased
Fault offset has increased greatly
Fault offset has increased
Fault offset should remain
unchanged
Sedimentary Key EventsTectonics Key Events
4) Basin organisation with reverse faulting and sinistral transpressional
event (Harris 1994)
1) Neo-proterzoic basement have undergone a series of structural events
that involved syn-rift sequences (Harris 2000, Song & Cawood 2000)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II meagsequence
(Norvick 2004)
3) Start of syn-rift II meagsequence (Norvick 2004)
5) No record of structural near NPB but only in regional scale (Harris 1994)
Tectonic forces is inferred and interpreted to be decreasing in strength
1) Pre-Cambrian structural activity on the basement which may
have a potential effect on the upcoming Permian units
(Harris 2000, Song & Cawood 2000)
3) Abrupt change in sediment source, resulting in the start of the
deposition of Kockatea Shale (Cawood and Nemchin 2000)
5) Deposition should have appeared in between two discrete fault
4) Local thickening of units over the Mid-Triassic period
(Norvick 2004)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II
megasequence (Cawood and Nemchin 2000)
Regional Thickening
Direction
SW to NE
(700m - 1000m)
S to NE
(50m - 200m)
NW to SE
(50m - 200m)
N-NW to S-SE
(150m - 200m)
N to S
(150m - 200m)
Slight syn-sedimentary tectonics due to the presence of fault
controlled thickening (Leseur Sandstone Formation)
Synchronous Rule I or
even sedimentary deposition
Fault offset has increased
N to S
(300m - 400m)
E to W
(1500m - 2500m)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 43 / 55
(Jonathan Poh et al. in prep.)
111. From tectonic and sedimentary evolution to geological rules
Sedimentary
Low High
1
3
4
5
6
Tectonics
Low High
Permian
EarlyLate
Triassic
EarlyLateMid
Jurassic
EarlyLateMid
Cretaceous
EarlyLate
1
2
3
4
5
6
7
8
9
10 7
2
Breakupof
Gondwana
Geological Evolution Combination Applicable Rules Fault Offset Result
Multiple cycles of syn-tectonic sedimentary deposition with
a decrease in effect from sedimentary processes
(Early Permian sequence)
Syn-depositional tectonics with a strong normal faulting
component and a gradually increasing sedimentary process
(Late Permian Sequence)
Syn-depositional tectonics with a decrease in tectonic strength
(reverse faulting took place), sedimentary processes is
assumed to be stablised (Kockatea Shale)
Syn-sedimentary tectonics with a low tectonic strength
(reverted to normal faulting), sedimentary processes have
stablised (Woodada Formation)
Syn-tectonic sedimentary with an slight increased strength
from minor fault event (Eneabba Formation)
Normal Fault + Sedimentary + Normal Fault
(Cattamarra Coal Formation)
Inferred weak sedimentary and tectonic sedimentary
(Cadda Formation)
Syn-sedimentary tectonics with inferred strong sedimentary
and regional tectonic forces (Yarragadee Formation)
Synchronous Rule II (a)
Synchronous Rule II (b)
Synchronous Rule III (b)
Synchronous Rule I, IV or
even sedimentary deposition
Synchronous Rule I
Discrete Rule VI
Synchronous Rule I
Synchronous Rule I
(with litho-stratigraphic unit)
Fault offset becomes more
pronounced
Fault offset has increased and
should be greater than the fault
offset during the Early Permian
Fault offset has decreased
Fault offset has increased
Fault offset has increased
Fault offset has increased greatly
Fault offset has increased
Fault offset should remain
unchanged
Sedimentary Key EventsTectonics Key Events
4) Basin organisation with reverse faulting and sinistral transpressional
event (Harris 1994)
1) Neo-proterzoic basement have undergone a series of structural events
that involved syn-rift sequences (Harris 2000, Song & Cawood 2000)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II meagsequence
(Norvick 2004)
3) Start of syn-rift II meagsequence (Norvick 2004)
5) No record of structural near NPB but only in regional scale (Harris 1994)
Tectonic forces is inferred and interpreted to be decreasing in strength
1) Pre-Cambrian structural activity on the basement which may
have a potential effect on the upcoming Permian units
(Harris 2000, Song & Cawood 2000)
3) Abrupt change in sediment source, resulting in the start of the
deposition of Kockatea Shale (Cawood and Nemchin 2000)
5) Deposition should have appeared in between two discrete fault
4) Local thickening of units over the Mid-Triassic period
(Norvick 2004)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II
megasequence (Cawood and Nemchin 2000)
Regional Thickening
Direction
SW to NE
(700m - 1000m)
S to NE
(50m - 200m)
NW to SE
(50m - 200m)
N-NW to S-SE
(150m - 200m)
N to S
(150m - 200m)
Slight syn-sedimentary tectonics due to the presence of fault
controlled thickening (Leseur Sandstone Formation)
Synchronous Rule I or
even sedimentary deposition
Fault offset has increased
N to S
(300m - 400m)
E to W
(1500m - 2500m)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 43 / 55
(Jonathan Poh et al. in prep.)
112. From tectonic and sedimentary evolution to geological rules
Sedimentary
Low High
1
3
4
5
6
Tectonics
Low High
Permian
EarlyLate
Triassic
EarlyLateMid
Jurassic
EarlyLateMid
Cretaceous
EarlyLate
1
2
3
4
5
6
7
8
9
10 7
2
Breakupof
Gondwana
Geological Evolution Combination Applicable Rules Fault Offset Result
Multiple cycles of syn-tectonic sedimentary deposition with
a decrease in effect from sedimentary processes
(Early Permian sequence)
Syn-depositional tectonics with a strong normal faulting
component and a gradually increasing sedimentary process
(Late Permian Sequence)
Syn-depositional tectonics with a decrease in tectonic strength
(reverse faulting took place), sedimentary processes is
assumed to be stablised (Kockatea Shale)
Syn-sedimentary tectonics with a low tectonic strength
(reverted to normal faulting), sedimentary processes have
stablised (Woodada Formation)
Syn-tectonic sedimentary with an slight increased strength
from minor fault event (Eneabba Formation)
Normal Fault + Sedimentary + Normal Fault
(Cattamarra Coal Formation)
Inferred weak sedimentary and tectonic sedimentary
(Cadda Formation)
Syn-sedimentary tectonics with inferred strong sedimentary
and regional tectonic forces (Yarragadee Formation)
Synchronous Rule II (a)
Synchronous Rule II (b)
Synchronous Rule III (b)
Synchronous Rule I, IV or
even sedimentary deposition
Synchronous Rule I
Discrete Rule VI
Synchronous Rule I
Synchronous Rule I
(with litho-stratigraphic unit)
Fault offset becomes more
pronounced
Fault offset has increased and
should be greater than the fault
offset during the Early Permian
Fault offset has decreased
Fault offset has increased
Fault offset has increased
Fault offset has increased greatly
Fault offset has increased
Fault offset should remain
unchanged
Sedimentary Key EventsTectonics Key Events
4) Basin organisation with reverse faulting and sinistral transpressional
event (Harris 1994)
1) Neo-proterzoic basement have undergone a series of structural events
that involved syn-rift sequences (Harris 2000, Song & Cawood 2000)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II meagsequence
(Norvick 2004)
3) Start of syn-rift II meagsequence (Norvick 2004)
5) No record of structural near NPB but only in regional scale (Harris 1994)
Tectonic forces is inferred and interpreted to be decreasing in strength
1) Pre-Cambrian structural activity on the basement which may
have a potential effect on the upcoming Permian units
(Harris 2000, Song & Cawood 2000)
3) Abrupt change in sediment source, resulting in the start of the
deposition of Kockatea Shale (Cawood and Nemchin 2000)
5) Deposition should have appeared in between two discrete fault
4) Local thickening of units over the Mid-Triassic period
(Norvick 2004)
2) End of Syn-rift megasequence I found through an unconformity
at Caryngina Formation and the start of syn-rift II
megasequence (Cawood and Nemchin 2000)
Regional Thickening
Direction
SW to NE
(700m - 1000m)
S to NE
(50m - 200m)
NW to SE
(50m - 200m)
N-NW to S-SE
(150m - 200m)
N to S
(150m - 200m)
Slight syn-sedimentary tectonics due to the presence of fault
controlled thickening (Leseur Sandstone Formation)
Synchronous Rule I or
even sedimentary deposition
Fault offset has increased
N to S
(300m - 400m)
E to W
(1500m - 2500m)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 43 / 55
(Jonathan Poh et al. in prep.)
113. North Perth Basin - first results, unvalidated models
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 44 / 55
Next step: parameterise and add constraints
114. Combining probabilistic modelling with resource
estimations
Probabilistic geothermal resource assessment
Geothermal resource estimation for
North Perth Basin model with
estimation of uncertainty:
Simulate temperature field for
all valid models
calculate geothermal resource
(heat in place)
Preliminary results, presented at
Australian Geothermal Energy Conference
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 45 / 55
115. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
116. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
117. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
118. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
119. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
Quantify geological rules
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
120. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
Quantify geological rules
Perform rejection sampling for automatic model validation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
121. Conclusion from application to NPB
Application to North Perth Basin
Possible to separate significant phases from geological evolution to
derive constraints
Python workflow for stochastic simulations works for (reasonably)
complex models
Combination with geothermal resource estimation feasible
Next steps
Define probability distributions for all data points
Quantify geological rules
Perform rejection sampling for automatic model validation
Compare differences in geothermal resource estimation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 46 / 55
122. Outlook and Future Work
3-D Geological
Modelling
Uncertainties
Probabilistic framework
for multiple constraints
Model validation and
geological “rules”
Application: North
Perth Basin
Future work
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 47 / 55
128. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55
129. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
Discontinuities
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55
130. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
Discontinuities
...
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55
131. Geologic topology
Considerations of geological topology vs. geometric topology
How to characterise topological
elements with a geologic meaning?
Fault surfaces
Discontinuities
...
?
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 51 / 55
132. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55
133. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55
134. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
Faulting
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55
135. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
Faulting
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55
136. Combination with kinematic modelling
Using Noddy for kinematic modelling to parameterise geological
knowledge
Start with a stratigraphic
pile
Add geological history
events, for example:
Folding
Faulting
Idea: use as stochastic model to generate typical probability
distributions expected for specific events (simplest case: fault offset, as
used before!)
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 52 / 55
137. Combining geological modelling and multiphase flow
simulations
Combined inversion of structural interpolation and fluid flow
simulation
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 53 / 55
138. Combination with Seismics: Madagascar
Combining implicit geological modelling with seismic simulations
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 54 / 55
139. Thank you
(3D Interest Group) Uncertainties in 3-D Structural Models March 1, 2014 55 / 55