3D digital outcrop modelling of the Lower Cretaceous Los Santos formation sandstones, Mesa de Los Santos region (Colombia): Implications for structural analysis. (December 2020)

• Record Type: Journal Article
• Authors: Villarreal J, Carlos Alberto; Rojas D, Julián David; Ríos R, Carlos Alberto
• Is Part Of: Journal of structural geology. Volume 141(2020)
• Publication Date: 2020-12
• Subjects: Digital outcrop -- Structural analysis -- Los Santos formation -- Sandstones -- Modelling; Geology, Structural -- Periodicals; Géomorphologie structurale -- Périodiques; Geology, Structural; Periodicals; 551.805
• DOI: 10.1016/j.jsg.2020.104214
• Languages: English
• ISSNs: 0191-8141

Abstract

Abstract: The increasing development of the geological data acquisition and processing techniques for outcrops from photographs acquired with the unmanned aerial vehicles has had a great impact on various branches of geology, such as structural geology. When approaching structural analysis, one of the main inputs is given by the fracture orientations. Therefore, the use of unmanned aerial vehicles is a great benefit for acquiring in a very short time, a large georeferenced dataset for fractures distribution and orientation, even in normally non-accessible outcrops. Access during the fieldwork, as well as the ease of data processing and visualization, constitute great advantages provided by the use of unmanned aerial vehicles in structural analysis and modelling. In this study, data were acquired from two outcrops from the Lower Cretaceous Los Santos Formation sandstones, Mesa de Los Santos region (Colombia), El Duende and La Mojarra outcrops. The data was processed in photogrammetry software, based on the structure from motion technique, with which a dense point cloud was obtained for each outcrop. The dense point cloud was processed with the Discontinuity Set Extractor software, obtaining the main families of discontinuities and their associated clusters. Later, as a validation, these orientations, which obtained in a digital way, were contrasted with field data, previously acquired in the area, obtaining a good consistency between both datasets. Finally, the data was associated with the regional structural trends evidenced in the field data, generating more data to improve the structural models of the area.