This paper introduces a methodology based on Python libraries and machine learning k-Nearest Neighbors (KNN) algorithms to create an interactive 3D HTML model (3D_Vertical_Sections_Faults_LRD.html) that combines 2D grain-size KNN-prediction vertical maps (vertical sections) from which syn-sedimentary faults and other features in sedimentary porous media can be delineated. The model can be visualized and handled with […]
The k-nearest neighbors (KNN) algorithm is a non-parametric supervised machine learning classifier; which uses proximity and similarity to make classifications or predictions about the grouping of an individual data point. This ability makes the KNN algorithm ideal for classifying datasets of geological variables and parameters prior to 3D visualization. This paper introduces a machine learning […]
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A Python application for visualizing the 3D stratigraphic architecture of porous sedimentary media has been developed. The application uses the parameter granulometry deduced from borehole lithological records to create interactive 3D HTML models of essential stratigraphic elements. On the basis of the high density of boreholes and the subsequent geological knowledge gained during the last six decades, the […]
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