Seismic and acoustic imaging of fluid seepage structures in different sedimentological and tectonic settings in the Lower Congo Basin

Using various geophysical methods including multichannel seismics, bathymetric mapping and sediment echosounding, several active gas seepage sites on the seafloor in the deep Lower Congo Basin were investigated. Gas is formed within Oligocene to Miocene fan deposits and relies on migration pathways to reach the seafloor. To the South of the Congo Canyon, continued seaward moving deformation induced by Aptian salt movement is shown to promote active gas seepage at the deformation front while further landward sites remain inactive. In the vicinity of the post-Miocene Congo Fan to the North of the Congo Canyon, active seepage can be shown to depend on salt tectonic faulting that connects gas-charged Pliocene fan deposits to the seafloor in the absence of widespread polygonal faulting in hemipelagic sediments. These investigated seepage sites act as an example of possible gas seepage configurations at the front of compressional regimes that can be applied to other similar areas

Shipping Configuration Optimization with Topology-Based Guided Local Search for Irregular Shaped Shipments

Manufacturer that uses containers to ship products always works to optimize the space inside the containers. Container loading problems (CLP) are widely encountered in forms of raw material flow and handling, product shipments, warehouse management, facility floor planning, as well as strip-packing nesting problems.Investigations and research conducted two decades ago were logistic orientated, on the basis of the empirical approaches

Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration