3D Visualization of Sparse Geophysical Data Representing Uncertainty

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dc.contributor.authorGonçalves, Vítoren_US
dc.contributor.authorDias, Pauloen_US
dc.contributor.authorAlmeida, Fernandoen_US
dc.contributor.authorMadeira, Joaquimen_US
dc.contributor.authorSantos, Beatriz Sousaen_US
dc.contributor.editorSilva, F. and Gutierrez, D. and Rodríguez, J. and Figueiredo, M.en_US
dc.date.accessioned2021-06-18T07:47:41Z
dc.date.available2021-06-18T07:47:41Z
dc.date.issued2021
dc.description.abstractGeophysical data are sparse and by nature difficult to analyze. Usually domain experts use "mental models" to infer missing data according to the surrounding data and their own knowledge. The main goal of this work is to explore the best way to represent uncertainty in geophysical data. Given the sparse nature of the represented data, it is important to provide a 3D volumetric representation of the whole subsoil, based on a geostatistical process. We use kriging interpolation to generate a structured grid from the original sparse data. However, the analysis of such an interpolated representation must be careful, since the uncertainty varies significantly according to the distance to real measurements. We use different representations to emphasize data uncertainty during the analysis stage. The different visualization techniques implemented in our prototype, as well as methods used to simultaneously visualize resistivity and uncertainty information, are presented.en_US
dc.description.sectionheadersData Visualization
dc.description.seriesinformationV Ibero-American Symposium in Computer Graphics
dc.identifier.doi10.2312/pt.20111124
dc.identifier.isbn978-3-03868-152-6
dc.identifier.pages23-29
dc.identifier.urihttps://doi.org/10.2312/pt.20111124
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/pt20111124
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectVisualization
dc.subjectuncertainty visualization
dc.subjectgeophysical data
dc.subjectVTK (Visualization Toolkit)
dc.title3D Visualization of Sparse Geophysical Data Representing Uncertaintyen_US
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