Digital 3D Rocks: A Collaborative Benchmark for Learning Rocks Recognition

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/91871
Información del item - Informació de l'item - Item information
Title: Digital 3D Rocks: A Collaborative Benchmark for Learning Rocks Recognition
Authors: Riquelme, Adrián | Cano, Miguel | Tomás, Roberto | Jordá Bordehore, Luis | Pastor Navarro, José Luis | Benavente, David
Research Group/s: Ingeniería del Terreno y sus Estructuras (InTerEs) | Petrología Aplicada
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Civil | Universidad de Alicante. Departamento de Ciencias de la Tierra y del Medio Ambiente
Keywords: Geology | Remote sensing | Computer graphics
Knowledge Area: Ingeniería del Terreno | Petrología y Geoquímica
Issue Date: 14-May-2019
Publisher: Springer Vienna
Citation: Rock Mechanics and Rock Engineering. 2019. doi:10.1007/s00603-019-01843-3
Abstract: Naked eye rock recognition is an essential activity for professionals and students of geosciences, architecture and engineering. Through a hand holding rock specimen, it is usually required not only to identify the type of rock but recognize their texture and understand its expected properties mechanical and petrophysical properties. Although a wide choice of books, websites and apps are available in the literature and on the Internet, their contents are two-dimensional (2D) and static. Nowadays, the application of remote sensing techniques such as Light Detection and Ranging (LiDAR) or Structure from Motion (SfM) enable the generation of three-dimensional (3D) interactive models, which are here presented as a novel perspective of learning and practising rocks recognition. Despite limitations of the technique, 3D digital models of rocks permit their virtual visualization and manipulation to reveal parts of the specimens that are hidden in the 2D photograph, as well as details of the rock specimen’s texture such as grain and minerals size, distribution and organization along with the possibility of identifying petrological features, foliation, mineral orientations and others. This provides a novel perspective of learning and practising rocks identification. Herein, a benchmark of digital rocks collected all around the world and generated using SfM technique is presented. The rocks are organised using a straightforward classification system based on the texture jointly with a detailed description to aid the specimen recognition. A behavioural geomechanical classification is then applied. Moreover, a linked datasheet shows the engineering classification, the weathering degree, the guide physical and mechanical properties (general, and specific when available), the engineering uses and others. The information is organised on an open-access website hosted by the University of Alicante (https://web.ua.es/digitalrocks). This initiative also aims to encourage students and professionals to generate their own models and to provide the description to enlarge the repository.
Sponsor: This work was partially funded by the University of Alicante (vigrob-157 Project, GRE14-4, GRE15-19 and GRE17-011 Projects), the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P. Authors thank Ignacio Pérez-Rey/ Leandro Alejano for the description some used samples.
URI: http://hdl.handle.net/10045/91871
ISSN: 0723-2632 (Print) | 1434-453X (Online)
DOI: 10.1007/s00603-019-01843-3
Language: eng
Type: info:eu-repo/semantics/article
Rights: © Springer-Verlag GmbH Austria, part of Springer Nature 2019
Peer Review: si
Publisher version: https://doi.org/10.1007/s00603-019-01843-3
Appears in Collections:INV - PETRA - Artículos de Revistas
INV - INTERES - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2019_Riquelme_etal_RockMechRockEng_final.pdfVersión final (acceso restringido)1,25 MBAdobe PDFOpen    Request a copy
Thumbnail2019_Riquelme_etal_RockMechRockEng_revised.pdfEmbargo 12 meses (acceso abierto: 15 mayo 2020)3,25 MBAdobe PDFOpen    Request a copy


Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.