4/30/2023 0 Comments Fractured space pioneer stats![]() In addition, the increase in the amount of geochemical data from state-of-the-art instruments (e.g. To be realistic, computational experiments commonly rely on an algorithmic or mechanistic approach rather than deriving a mathematical analytical solution to the problem. In petrology and geochemistry, a large variety of computational models have been developed to simulate and study these processes. Numerical modeling has become a critical aspect of modern research, especially in Earth Sciences as physical and chemical processes occurring in planetary interiors are not always directly observable from the surface. Overall, this session focuses on mathematical and algorithmic approaches for characterizing geological media and/or discrete fracture networks, in terms of topology, morphology, hydraulic/conduction properties, based on field observations as well as models. This topic is also related to upscaling issues in fractured media and networks : these are relevant to this session in connexion with morphology and structure. Extensions to other mechanisms focusing on morphology and structure of discrete fracture networks are also welcome (e.g., two-phase flow, mechanical deformation, electrical conduction…). as random Boolean objects), and/or, based on graph theory concepts. These generalized geological "networks" may be described deterministically or statistically (e.g. This session is an opportunity to present various approaches for analyzing geometrical, topological, and hydraulic properties of 3D Discrete Planar Fracture Networks (DPFN), or their 2D counterpart (e.g., discrete flow networks represented by intersecting segments in the plane), or other discrete sets of Boolean objects (conductors, barriers, cavities, etc.). "Fractured geological media and fracture networks: flow, graphs, morphology." This session welcomes submissions of all the topics mentioned above, to provide a venue for knowledge graph practitioners to share their results and experience, learn best practices, and discuss visions and potential collaborations for future work. Together, knowledge graphs have shown promising contribution to data science applications in geosciences, and more innovative developments are undergoing. Recently, there were also applications of knowledge graph together with machine learning algorithms to improve the quality of data analytics, such as those in hyperspectral remote sensing image processing, extension to mineral taxonomy, and petroleum exploration. In the geoscience community, we have seen many successful applications of knowledge graph in data curation and integration in the past decades, such as the global geologic map sharing enabled by OneGeology. Inspired by the recent success of knowledge graphs in industry, the academia is beginning to use knowledge graph as an umbrella topic for works on vocabularies, schemas, and ontologies. Vocabularies, schemas and ontologies have been increasingly created and applied in geosciences in the past decades, and have always been a topic of interest in geoinformatics. The mini-symposium will bring together researchers working on fundamental and applied aspects of machine learning and quantum computing to provide a forum for discussion, interaction, and assessment of their presented techniques. Topics include, but are not limited to, (1) machine learning algorithms and applications for model-reduction, optimization, inverse problems, uncertainty quantification, highly parameterized problems (e.g., parametrization of heterogeneous fields), and efficient dimensionality reduction of nonlinear operators and (2) quantum computing applications in geoscience research for instance, seismic inversion with quantum annealing, quantum-computational hydrologic inverse analysis, or quantum optimization. This mini-symposium invites presentations on advances within areas of machine learning and/or quantum computing in geoscience research. These approaches have been adopted and proposed to tackle long-standing challenges in geoscience or an enhancement of classical methods that have been used in this field. Recent advancements in machine learning techniques and quantum computing have made their way into geoscience research.
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