The eastern side of the Guadalupe Mountains, located in New Mexico and west Texas, represents an erosional profile along the Capitan reef margin. A complete shelf-to-basin exposure of the Upper Permian Capitan shelf margin is found on the north wall of North McKittrick Canyon, which is nearly perpendicular to the Capitan reef margin. An excellent 2-D sequence stratigraphic framework for upper Permian backreef facies has been developed by previous workers for North McKittrick Canyon (Tinker, 1998) and Slaughter Canyon (Osleger, 1998), forming the basis for observations in this study. The goal of this study is to describe the sequence stratigraphic architecture of the Yates Formation, focusing on the Y4-Y6 high-frequency sequences (HFSs) found in the middle to outer shelf depositional systems, and to illustrate the use of airborne lidar data to quantitatively map at the cycle-scale. Seven measured sections were taken in North McKittrick Canyon. From airborne lidar, 3-D geometries of key sedimentary and structural features were mapped in Polyworks, in addition to the sequence boundaries delineating the Yates 4-6 HFSs. In general, major cycles exhibit asymmetry and shoal upward. Cycle boundaries are sometimes hard to delineate due to amalgamation, particularly in the shelf crest. High-frequency sequences are commonly asymmetric; they deepen and thicken upward toward the maximum flooding surface, and the boundaries between HFSs are usually marked by thick siltstones. Major HFS boundaries can be mapped across the entire dataset, and some component cycles can be observed for minimum distances of one kilometer in an updip-downdip direction. Also, some facies tract dimensions can be estimated directly from the lidar data. Measured sections indicate that the shelf crest facies tract shifts seaward with each successive HFS, while the outer shelf facies tract steps landward. Future work that could be done with the Y4-Y6 HFSs includes 8-10 more measured sections, collection of samples for thin sections, and tracing out of contacts between facies tracts. Extensive lidar data interpretation needs to be done so that digital outcrop models demonstrating facies distributions can be produced. This would enable the development of an outcrop analog model to mixed carbonate-siliciclastic reservoirs, which would be unprecedented in this area. / text

3D semantic segmentation is an expanding topic within the field of computer vision, which has received more attention in recent years due to the development of more powerful GPUs and the newpossibilities offered by deep learning techniques. Simultaneously, the amount of available spatial LiDAR data over Sweden has also increased. This work combines these two advances and investigates if a 3D deep learning model for semantic segmentation can learn to detect terrain roughness in airborne LiDAR data. The annotations for terrain roughness used in this work are taken from SGUs 2D soil type map. Other airborne data sources are also used to filter the annotations and see if additional information can boost the performance of the model.  Since this is the first known attempt at terrain roughness classification from 3D data, an initial test was performed where fields were classified. This ensured that the model could process airborne LiDAR data and work for a terrain classification task. The classification of fields showed very promising results without any fine-tuning. The results for the terrain roughness classification task show that the model could find a pattern in the validation data but had difficulty generalizing it to the test data. The filtering methods tested gave an increased mIoU and indicated that better annotations might be necessary to distinguish terrain roughness from other terrain types. None of the features obtained from the other data sources improved the results and showed no discriminating abilities when examining their individual histograms. In the end, more research is needed to determine whether terrain roughness can be detected from LiDAR data or not.