A K-Nearest Neighbors Algorithm in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain
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Título: | A K-Nearest Neighbors Algorithm in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain |
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Autor/es: | Bullejos, Manuel | Cabezas, David | Martín-Martín, Manuel | Alcalá, Francisco J. |
Grupo/s de investigación o GITE: | Evolución Geodinámica de la Cordillera Bética Oriental y de la Plataforma Marina de Alicante |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencias de la Tierra y del Medio Ambiente |
Palabras clave: | Llobregat River Delta | 3D stratigraphic architecture | Granulometry | KNN algorithm | Python libraries |
Fecha de publicación: | 19-jul-2022 |
Editor: | MDPI |
Cita bibliográfica: | Bullejos M, Cabezas D, Martín-Martín M, Alcalá FJ. A K-Nearest Neighbors Algorithm in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain. Journal of Marine Science and Engineering. 2022; 10(7):986. https://doi.org/10.3390/jmse10070986 |
Resumen: | 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 KNN algorithm and Python libraries for visualizing the 3D stratigraphic architecture of sedimentary porous media in the Quaternary onshore Llobregat River Delta (LRD) in northeastern Spain. A first HTML model showed a consecutive 5 m-equispaced set of horizontal sections of the granulometry classes created with the KNN algorithm from 0 to 120 m below sea level in the onshore LRD. A second HTML model showed the 3D mapping of the main Quaternary gravel and coarse sand sedimentary bodies (lithosomes) and the basement (Pliocene and older rocks) top surface created with Python libraries. These results reproduce well the complex sedimentary structure of the LRD reported in recent scientific publications and proves the suitability of the KNN algorithm and Python libraries for visualizing the 3D stratigraphic structure of sedimentary porous media, which is a crucial stage in making decisions in different environmental and economic geology disciplines. |
Patrocinador/es: | Research Project PID2020-114381GB-100 of the Spanish Ministry of Science and Innovation, Research Groups and Projects of the Generalitat Valenciana from the University of Alicante (CTMA-IGA), and Research Groups FQM-343 and RNM-188 of the Junta de Andalucía. |
URI: | http://hdl.handle.net/10045/125553 |
ISSN: | 2077-1312 |
DOI: | 10.3390/jmse10070986 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/jmse10070986 |
Aparece en las colecciones: | INV - GEODIN - Artículos de Revistas |
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