Geological surveying is a common practice performed by geologists nowadays. Determining layer orientation parameters and identifying folds are examples of features needed in order to create a geological map or model of the (sub) surface. Surveying is expensive and time consuming, therefore such survey is recently being investigated and implemented in 3D point clouds. This thesis provides a method to quickly obtain geological information such as orientation parameters and fold information in a semi-automated manner. Obtaining this parameters is currently still a difficult problem because of data density and calculation complexity.
Fast and reliable are terms important for the created method. Large point clouds can easily jam a system and reliability is important for usability and compatibility of the created results. A method is designed which can handle bulk data, performing operations such as filtering and noise reduction assisting in data handling. Next to general calculations such as roughness filtering, intensity filtering and directional filtering of generated normals, the use of a Graphical User Interface (GUI) greatly improves the reliability of the feature calculations. The user selects areas which are potentially interesting for acquiring layer orientation information, which are then analyzed by the method itself creating a base for determining the layer orientation parameters. This base is then used to attempt the reconstruction of the bedding layers of the geological outcrop resulting in the desired geological features.
The method has been tested on the “La Charce” dataset and the “Meso-scaled fold” dataset for method validation and on the “Pradelle 2” dataset for compatibility checking. Average calculation time is with approximately 10 minutes reasonable fast. The acquired dip direction of approximately 156 degrees is very similar to that of Prentice (2017), Bisschop (2017) and the fieldwork teams of 2016. The dip angle of 52 degrees is on the other hand off compared to validation results. The layer thickness calculations are comparable, all theses show that the bulk thicknesses between ~30 and ~70 cm. The fold dataset has no validation results but the images look good. It has to be mentioned that all results obtain commission and omission errors but these are in no comparison to the results which are correct.