Relevant bibliographies by topics / Computational geoscience

Academic literature on the topic 'Computational geoscience'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Computational geoscience"

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Zhao, Chongbin. "Computational Methods for Simulating Some Typical Problems in Computational Geosciences." International Journal of Computational Methods 13, no. 02 (March 2016): 1640016. http://dx.doi.org/10.1142/s0219876216400168.

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The main purpose of this paper is to present computational methods for simulating some typical problems in the emerging computational geoscience field. Due to remarkable differences between engineering systems and Earth ones, existing computational methods, which are designed for solving engineering problems, cannot be directly used to solve geoscience problems without any modification. However, the fundamental philosophy of developing computational methods is applicable to the computational simulation of both geoscience and engineering problems. Because of their inherent approximation, computational methods must be verified before putting into application. After several computational methods and algorithms, which are developed for simulating some typical problems in the emerging computational geoscience field, are briefly introduced, a typical geoscience problem, known as the chemical dissolution-front instability problem in ore-forming systems of supercritical Zhao numbers, is selected to demonstrate how computational methods can be used to solve geoscience problems.
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Savelonas, Michalis A., Christos N. Veinidis, and Theodoros K. Bartsokas. "Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey." Remote Sensing 14, no. 23 (November 27, 2022): 6017. http://dx.doi.org/10.3390/rs14236017.

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Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience objects and processes, as well as non-stationarity in space and/or time, provide an ideal test bed for advanced computer vision techniques. On the other hand, the developments in pattern recognition, especially with the rapid evolution of powerful graphical processing units (GPUs) and the subsequent deep learning breakthrough, enable valuable computational tools, which can aid geoscientists in important problems, such as land cover mapping, target detection, pattern mining in imaging data, boundary extraction and change detection. In this landscape, classical computer vision approaches, such as active contours, superpixels, or descriptor-guided classification, provide alternatives that remain relevant when domain expert labelling of large sample collections is often not feasible. This issue persists, despite efforts for the standardization of geoscience datasets, such as Microsoft’s effort for AI on Earth, or Google Earth. This work covers developments in applications of computer vision and pattern recognition on geoscience-related imaging data, following both pre-deep learning and post-deep learning paradigms. Various imaging modalities are addressed, including: multispectral images, hyperspectral images (HSIs), synthetic aperture radar (SAR) images, point clouds obtained from light detection and ranging (LiDAR) sensors or digital elevation models (DEMs).
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Abokhodair, Abdulwahab A. "Numerical tools for geoscience computations: Semiautomatic differentiation—SD." Computational Geosciences 11, no. 4 (June 5, 2007): 283–96. http://dx.doi.org/10.1007/s10596-007-9052-z.

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Blais, J. A. Rod. "Optimal Modeling and Filtering of Stochastic Time Series for Geoscience Applications." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/895061.

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Sequences of observations or measurements are often modeled as realizations of stochastic processes with some stationary properties in the first and second moments. However in practice, the noise biases and variances are likely to be different for different epochs in time or regions in space, and hence such stationarity assumptions are often questionable. In the case of strict stationarity with equally spaced data, the Wiener-Hopf equations can readily be solved with fast Fourier transforms (FFTs) with optimal computational efficiency. In more general contexts, covariance matrices can also be diagonalized using the Karhunen-Loève transforms (KLTs), or more generally using empirical orthogonal and biorthogonal expansions, which are unfortunately much more demanding in terms of computational efforts. In cases with increment stationarity, the mathematical modeling can be modified and generalized covariances can be used with some computational advantages. The general nonlinear solution methodology is also briefly overviewed with the practical limitations. These different formulations are discussed with special emphasis on the spectral properties of covariance matrices and illustrated with some numerical examples. General recommendations are included for practical geoscience applications.
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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "Advances in computational geoscience: Numerical methods and algorithms for simulating geofluid flow related problems." Journal of Geochemical Exploration 101, no. 1 (April 2009): 127. http://dx.doi.org/10.1016/j.gexplo.2008.11.022.

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Leung, Raymond. "Subsurface Boundary Geometry Modeling: Applying Computational Physics, Computer Vision, and Signal Processing Techniques to Geoscience." IEEE Access 7 (2019): 161680–96. http://dx.doi.org/10.1109/access.2019.2951605.

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Frings, Patrick J., and Heather L. Buss. "The Central Role of Weathering in the Geosciences." Elements 15, no. 4 (August 1, 2019): 229–34. http://dx.doi.org/10.2138/gselements.15.4.229.

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Weathering is the chemical and physical alteration of rock at the surface of the Earth, but its importance is felt well beyond the rock itself. The repercussions of weathering echo throughout the Earth sciences, from ecology to climatology, from geomorphology to geochemistry. This article outlines how weathering interacts with various geoscience disciplines across a huge range of scales, both spatial and temporal. It traces the evolution of scientific thinking about weathering and man's impact on weathering itself—for better and for worse. Future computational, conceptual and methodological advances are set to cement weathering's status as a central process in the Earth sciences.
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Kumi-Boateng, Bernard, and Yao Yevenyo Ziggah. "FEASIBILITY OF USING GROUP METHOD OF DATA HANDLING (GMDH) APPROACH FOR HORIZONTAL COORDINATE TRANSFORMATION." Geodesy and cartography 46, no. 2 (July 9, 2020): 55–66. http://dx.doi.org/10.3846/gac.2020.10486.

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Machine learning algorithms have emerged as a new paradigm shift in geoscience computations and applications. The present study aims to assess the suitability of Group Method of Data Handling (GMDH) in coordinate transformation. The data used for the coordinate transformation constitute the Ghana national triangulation network which is based on the two-horizontal geodetic datums (Accra 1929 and Leigon 1977) utilised for geospatial applications in Ghana. The GMDH result was compared with other standard methods such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), 2D conformal, and 2D affine. It was observed that the proposed GMDH approach is very efficient in transforming coordinates from the Leigon 1977 datum to the official mapping datum of Ghana, i.e. Accra 1929 datum. It was also found that GMDH could produce comparable and satisfactory results just like the widely used BPNN and RBFNN. However, the classical transformation methods (2D affine and 2D conformal) performed poorly when compared with the machine learning models (GMDH, BPNN and RBFNN). The computational strength of the machine learning models’ is attributed to its self-adaptive capability to detect patterns in data set without considering the existence of functional relationships between the input and output variables. To this end, the proposed GMDH model could be used as a supplementary computational tool to the existing transformation procedures used in the Ghana geodetic reference network.
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Bhatt, Asti, Todd Valentic, Ashton Reimer, Leslie Lamarche, Pablo Reyes, and Russell Cosgrove. "Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration." Journal of Space Weather and Space Climate 10 (2020): 12. http://dx.doi.org/10.1051/swsc/2020011.

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The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multiple sources using methodologies and tools that are reproducible. The architecture of Resen combines two mainstream open source software tools, Docker and JupyterHub, to produce a software environment that not only facilitates computationally reproducible research results, but also facilitates effective collaboration among researchers. In this technical paper, we discuss some challenges for performing reproducible science and a potential solution via Resen, which is demonstrated using a case study of a geospace event. Finally we discuss how the usage of mainstream, open-source technologies seems to provide a sustainable path towards enabling reproducible science compared to proprietary and closed-source software.
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Vacher, H. L. "Computational Geology 13 – Geological-Mathematical Activities for College Students in the Journal of Geoscience Education, 1990–1999." Journal of Geoscience Education 48, no. 5 (November 2000): 682–91. http://dx.doi.org/10.5408/1089-9995-48.5.682.

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Dissertations / Theses on the topic "Computational geoscience"

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Ricchezza, Victor J. "Alumni Narratives on Computational Geology (Spring 1997 – Fall 2013)." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6366.

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Recent meetings and publications have discussed what geoscience undergraduates should learn for professional success, and among other items, have identified several quantitative skills and habits of mind as being necessary for geoscience students; many of these items are commonly associated with Quantitative Literacy (QL). The Computational Geology course in the geology department has been evolving at USF for 20 years. The course teaches QL in a geologic setting independent of specific core geology topics. This course has long preceded the national acknowledgment of the need for what it teaches within the field. As the first of a series of related studies intended to find the effect and role of this course within the geoscience community, this thesis study begins as a qualitative narrative inquiry of course and program alumni. In the study reported here ten USF Geology alumni from a variety of career paths who took GLY 4866 between 1997 to 2013 underwent semi-structured interviews recounting their memories of the course, discussing the benefits to them of the course in their careers, and outlining their views of what students should gain from this course for professional success. The interview results illuminate trends that can be usefully grouped by job/career category. Regulators (3) had the shortest overall interview time, remembered the least in terms of specific events from the course, and had limited (but consistent) suggestions for student learning. Their memories and suggestions were also rarely unique. Consultants (3) were the median group in length, and showed overlap in the content of their interviews to regulators, with additional details added. Academics (4) had the longest interview times, the most detailed memories from the course, and the most suggestions, possibly due to these interviewees using similar methods in their later careers as course instructors. Consultants and academics related large blocks of story text that were unique while also relating common statements. Narratives from professionally successful alumni were sought to gain greater detail on the likely impact of Computational Geology than surveys are likely to give. The responses of selected, successful alumni were also sought to help refine questions that are to be used later in surveys of a larger sample population of alumni and to a larger national audience of geoscientists regarding their undergraduate programs and how those programs prepared them with quantitative skills. The information that interview subjects provided about the educational needs for successful entry-level geology professionals were shaped into a series of suggestions for course and program improvement. Course and program improvement suggestions and questions for a proposed survey have been assembled both to improve the GLY 4866 offering at USF for broader dissemination and to contribute to broader discussion of strategies for improving the quantitative skills and learning of geoscientists.
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Books on the topic "Computational geoscience"

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Ord, Alison, Bruce E. Hobbs, and Chongbin Zhao. Fundamentals of Computational Geoscience. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9.

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E, Hobbs Bruce, and Ord Alison 1955-, eds. Fundamentals of computational geoscience: Numerical methods and algorithms. Berlin: Springer, 2009.

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1945-, Fitzgibbon W. E., Wheeler Mary F, and Society for Industrial and Applied Mathematics., eds. Computational methods in geosciences. Philadelphia: Society for Industrial and Applied Mathematics, 1992.

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Haneberg, William C. Computational Geosciences with Mathematica. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18554-0.

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Haneberg, William C. Computational geosciences with Mathematica. Berlin: Springer, 2004.

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Dawson, Clint, and Margot Gerritsen, eds. Computational Challenges in the Geosciences. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7434-0.

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Kuhn, Werner. Spatial information theory: Foundations of geographic information science ; international conference ; proceedings. Berlin: Springer, 2003.

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Hobbs, Bruce E., Chongbin Zhao, and Alison Ord. Fundamentals of Computational Geoscience: Numerical Methods and Algorithms. Springer, 2010.

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Haneberg, William. Computational Geosciences with Mathematica. Springer London, Limited, 2012.

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Haneberg, William C. Computational Geosciences with Mathematica. Springer, 2013.

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Book chapters on the topic "Computational geoscience"

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Haneberg, William C. "Special Plots for Geoscience Data." In Computational Geosciences with Mathematica, 25–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18554-0_2.

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Hasanov, Alemdar, and Balgaisha Mukanova. "Inverse Resistivity Problems in Computational Geoscience." In Handbook of Geomathematics, 1845–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54551-1_62.

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(Hasanoǧlu), AlemdarHasanov, and Balgaisha Mukanova. "Inverse Resistivity Problems in Computational Geoscience." In Handbook of Geomathematics, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-27793-1_62-2.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "Introduction." In Fundamentals of Computational Geoscience, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_1.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "A Progressive Asymptotic Approach Procedure for Simulating Steady-State Natural Convective Problems in Fluid-Saturated Porous Media." In Fundamentals of Computational Geoscience, 7–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_2.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "A Consistent Point-Searching Interpolation Algorithm for Simulating Coupled Problems between Deformation, Pore-Fluid Flow, Heat Transfer and Mass Transport Processes in Hydrothemal Systems." In Fundamentals of Computational Geoscience, 37–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_3.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "A Term Splitting Algorithm for Simulating Fluid-Rock Interaction Problems in Fluid-Saturated Hydrothermal Systems of Subcritical Zhao Numbers." In Fundamentals of Computational Geoscience, 73–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_4.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "A Segregated Algorithm for Simulating Chemical Dissolution Front Instabilities in Fluid-Saturated Porous Rocks." In Fundamentals of Computational Geoscience, 95–119. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_5.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "A Decoupling Procedure for Simulating Fluid Mixing, Heat Transfer and Non-Equilibrium Redox Chemical Reactions in Fluid-Saturated Porous Rocks." In Fundamentals of Computational Geoscience, 121–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_6.

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Zhao, Chongbin, Bruce E. Hobbs, and Alison Ord. "An Equivalent Source Algorithm for Simulating Thermal and Chemical Effects of Intruded Magma Solidification Problems." In Fundamentals of Computational Geoscience, 153–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89743-9_7.

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Conference papers on the topic "Computational geoscience"

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Zhou, Cheng, Cun Yang, Ruoshui Zhou, Xingmiao Yao, and Guangmin Hu. "Fault surface extraction based on computational topology." In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3745482.1.

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Louboutin, Mathias, Philipp Witte, Ali Siahkoohi, Gabrio Rizzuti, Ziyi Yin, Rafael Orozco, and Felix J. Herrmann. "Accelerating innovation with software abstractions for scalable computational geophysics." In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3750561.1.

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Li, Lewis, Tao Sun, and Julio De La Colina. "Geostatistical reservoir modeling using computational stratigraphy models on non-Cartesian grids." In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3751585.1.

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Vieira, Jeison, Silvia Costa Botelho, and Nelson Duarte Filho. "A Framework for Distributed Shared Memory - Case Study in Geoscience Applications on Clusters of Computers." In 2009 Third Southern Conference on Computational Modeling (MCSUL). IEEE, 2009. http://dx.doi.org/10.1109/mcsul.2009.17.

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Huo, Weibo, Yulin Huang, Jifang Pei, Jianyu Yang, and Yin Zhang. "Bistatic sea clutter returns generation with computational electromagnetic method." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127137.

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Sun, Zhongyi, Mingmin Chi, and Jon Atli Benediktsson. "Computational Efficiency Active Learning for classification of hyperspectral images." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730339.

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McLean, Fiona, Cathal Reilly, and Jacob Shearer. "Efficiency improvements in structural geology modelling through the implementation of computational visual workflows." In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3745087.1.

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Acito, N., G. Corsini, and M. Diani. "Computational load reduction for anomaly detection in hyperspectral images: An experimental comparative analysis." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423527.

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Acito, N., G. Corsini, M. Diani, and M. Greco. "Reducing Computational Complexity in Hyperspectral Anomaly Detection: a Feature Level Fusion Approach." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.466.

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Huang, Zhou, and Yu Fang. "A novel approach for geospatial computational task processing in Grid environment." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5653068.

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Reports on the topic "Computational geoscience"

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Yoon, Hongkyu, Alec Kucala, Kyung Chang, Mario Martinez, James Bean, Teeratorn Kadeethum, Maria Warren, et al. Computational Analysis of Coupled Geoscience Processes in Fractured and Deformable Media. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890064.

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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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Antoun, T., I. Lomov, and J. Morris. Input to the NSF Study on Computational Requirements in Geosciences. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/15014428.

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