Method and analysis for the upscaling of structural data |
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| Institution: | 1. Department of Mining Engineering, University of Chile, Santiago, Chile;2. Advanced Mining Technology Center, University of Chile, Santiago, Chile;3. CSIRO-Chile International Center of Excellence in Mining and Mineral Processing, Santiago, Chile;1. State Key Laboratory of Geological Processes and Mineral Resources and Institute of Earth Sciences, China University of Geosciences, Beijing 100083, PR China;2. CSIRO Earth Science and Resource Engineering, PO Box 1130, Bentley, WA 6102, Australia;3. School of Earth and Environment, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia;4. Department of Applied Geology, Curtin University, Bentley, WA 6845, Australia;5. Department of Geosciences, National Taiwan University, Taipei 106, Taiwan;6. Geological Survey of Norway-NGU, 7491 Trondheim, Norway;7. Department of Geology and Mineral Resources Engineering, Norwegian University of Science and Technology-NTNU, 7491 Trondheim, Norway;1. Aix-Marseille Université, CNRS, IRD, CEREGE UM34, 13545 Aix en Provence, France;2. IRSN, Avenue du Général Leclerc, BP 17, 92262 Fontenay-aux-Roses, France;3. TOTAL, CSTJF, Avenue Larribau, 64018 Pau, France;1. Departamento de Geometría y Topología, Universidad de Granada, 18071 Granada, Spain;2. Departamento de Matemática, Universidade de Brasília, 70910-900 Brasília, Brazil;1. Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, No. 19, Beitucheng Western Road, Chaoyang District, 100029, Beijing, PR China;2. Beijing Institute of Geology for Mineral Resources, 100012, Beijing, PR China;3. Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, 100029, PR China |
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| Abstract: | 3D geological models are created to integrate a set of input measurements into a single geological model. There are many problems with this approach, as there is uncertainty in all stages of the modelling process, from initial data collection to the approach used in the modelling scheme itself to calculate the geological model. This study looks at the uncertainty inherent in geological models due to data density and introduces a novel method to upscale geological data that optimises the information in the initial dataset. This method also provides the ability for the dominant trend of a geological dataset to be determined at different scales. By using self-organizing maps (SOM's) to examine the different metrics used to quantify a geological model, we allow for a larger range of metrics to be used compared to traditional statistical methods, due to the SOM's ability to deal with incomplete datasets. The classification of the models into clusters based on the geological metrics using k-means clustering provides a useful insight into the models that are most similar and models that are statistical outliers. Our approach is guided and can be calculated on any input dataset of this type to determine the effect that data density will have on a resultant model. These models are all statistical derivations that represent simplifications and different scales of the initial dataset and can be used to interrogate the scale of observations. |
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| Keywords: | Upscaling Uncertainty Implicit modelling Self-organising maps |
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