- 1Earth Sciences Dept., NOVA FCT, Caparica, Portugal4 (jckullberg@gmail.com)
- 2GeoBioTec, NOVA FCT, Caparica, Portugal
- 3Geosciences Center, University of Coimbra, Coimbra, Portugal
- 4Geosense, IPN, Coimbra, Portugal
Exploitation of ornamental rock from quarries requires innovative approaches that can provide sustainable access to construction materials. Within a quarry, rock types can vary significantly, and its assessment demands a detailed evaluation of its lithological variations and fracture network. Adequate resource management is critical to minimize product waste, improve process efficiency and increase its worth along the value chain.
Using Unmanned Aerial Vehicles (UAVs), operating at low level flights, equipped with high resolution RGB cameras, GPS and GNSS systems, in addition to real-time differential data (RTK) and GCP, image datasets were acquired to perform 4D reconstructions and interpret highly detailed morphologic and geologic features.
We report the results of rock typing obtained from Digital Elevation Models, Orthophotos, Point Clouds and Mesh datasets, such as fracture network characterization and cavity delineation, that provide essential information about penalizing features, mandatory for informed decision-making during exploitation. These include: 1) manual and automated fracture delineation; 2) rock typing; 3) optimization of the exploitation plan, 4) volumetric estimation of land and karst cavities; and 5) stock management.
We used segmentation techniques on point clouds to analyze structural discontinuities and identify faults and fractures at different scales, in the quarry extraction fronts. The applied algorithms enable the automatic extraction of geological planes and determine the geometric characteristics of the point cloud. The RGB color variation in the point clouds was also analyzed, enabling the delimitation of areas with different colors, which are generally associated with the degree of rock alteration. This analysis also allows the accurate detection of fracture networks. Results from image analysis allow individualising discrete types of rock and confidently extract fracture networks. The discrete features extracted from the models were subsequently validated with fieldwork at the quarries.
Acknowledgements: The authors thank Research Project PRR - Sustainable Stone by Portugal integrated on the Mobilizing Agenda and Fundação para a Ciência e a Tecnologia, I.P. (FCT), Portugal, through the research unit UIDB/04035/2020 - GeoBioTec.
How to cite: Kullberg, J., Pereira, R., Machadinho, A., and Santos, A.: 4D Digital Twin geological modelling for sustainable quarry management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17148, https://doi.org/10.5194/egusphere-egu25-17148, 2025.