Inner Shelf Sorted Bedforms: Long-Term Evolution and a New Hybrid Model

<p>Sorted bedforms are spatial extensive (100 m-km) features present on many inner continental shelves with subtle bathymetric relief (cm-m) and localized, abrupt variations in grain size (fine sand to coarse sand/gravel). Sorted bedforms provide nursery habitat for fish, are a control on benthic biodiversity, function as sediment reservoirs, and influence nearshore waves and currents. Research suggests these bedforms are a consequence of a sediment sorting feedback as opposed to the more common flow-bathymetry interaction. This dissertation addresses three topics related to sorted bedforms: 1) Modeling the long-term evolution of bedform patterns, 2) Refinement of morphological and sediment transport relations used in the sorted bedform model with `machine learning'; 3) Development of a new sorted bedform model using these new `data-driven' components.</p><p> Chapter 1 focuses on modeling the long term evolution of sorted bedforms. A range of sorted bedform model behaviors is possible in the long term, from pattern persistence to spatial-temporal intermittency. Vertical sorting (a result of pattern maturation processes) causes the burial of coarse material until a critical state of seabed coarseness is reached. This critical state causes a local cessation of the sorting feedback, leading to a self-organized spatially intermittent pattern, a hallmark of observed sorted bedforms. Various patterns emerge when numerical experiments include erosion, deposition, and storm events. </p><p> Modeling of sorted bedforms relies on the parameterization of processes that lack deterministic descriptions. When large datasets exist, machine learning (optimization tools from computer science) can be used to develop parameterizations directly from data. Using genetic programming (a machine learning technique) and large multisetting datasets I develop smooth, physically meaningful predictors for ripple morphology (wavelength, height, and steepness; Chapter 2) and near bed suspended sediment reference concentration under unbroken waves (Chapter 3). The new predictors perform better than existing empirical formulations. </p><p> In Chapter 3, the new components derived from machine learning are integrated into the sorted bedform model to create a `hybrid' model: a novel way to incorporate observational data into a numerical model. Results suggest that the new hybrid model is able to capture dynamics absent from previous models, specifically, the two observed end-member pattern modes of sorted bedforms (i.e., coarse material on updrift bedform flanks or coarse material in bedform troughs). However, caveats exist when data driven components do not have parity with traditional theoretical components of morphodynamic models, and I address the challenges of integrating these disparate pieces and the future of this type of `hybrid' modeling.</p>Dissertatio

Naval Mine Detection and Seabed Segmentation in Sonar Images with Deep Learning

Underwater mines are a cost-effective method in asymmetric warfare, and are commonly used to block shipping lanes and restrict naval operations. Consequently, they threaten commercial and military vessels, disrupt humanitarian aids, and damage sea environments. There is a strong international interest in using sonars and AI for mine countermeasures and undersea surveillance. High-resolution imaging sonars are well-suited for detecting underwater mines and other targets. Compared to other sensors, sonars are more effective for undersea environments with low visibility. This project aims to investigate deep learning algorithms for two important tasks in undersea surveillance: naval mine detection and seabed terrain segmentation. Our goal is to automatically classify the composition of the seabed and localise naval mines. This research utilises the real sonar data provided by the Defence Science and Technology Group (DSTG). To conduct the experiments, we annotated 150 sonar images for semantic segmentation; the annotation is guided by experts from the DSTG.We also used 152 sonar images with mine detection annotations prepared by members of Centre for Signal and Information Processing at the University of Wollongong. Our results show Faster-RCNN to achieve the highest performance in object detection. We evaluated transfer learning and data augmentation for object detection. Each method improved our detection models mAP by 11.9% and 16.9% and mAR by 17.8% and 21.1%, respectively. Furthermore, we developed a data augmentation algorithm called Evolutionary Cut-Paste which yielded a 20.2% increase in performance. For segmentation, we found highly-tuned DeepLabV3 and U-Nett++models perform best. We evaluate various configurations of optimisers, learning rate schedules and encoder networks for each model architecture. Additionally, model hyper-parameters are tuned prior to training using various tests. Finally, we apply Median Frequency Balancing to mitigate model bias towards frequently occurring classes. We favour DeepLabV3 due to its reliable detection of underrepresented classes as opposed to the accurate boundaries produced by U-Nett++. All of the models satisfied the constraint of real-time operation when running on an NVIDIA GTX 1070

Effect of sediment load boundary conditions in predicting sediment Delta of Tarbela Reservoir in Pakistan

Setting precise sediment load boundary conditions plays a central role in robust modeling of sedimentation in reservoirs. In the presented study, we modeled sediment transport in Tarbela Reservoir using sediment rating curves (SRC) and wavelet artificial neural networks (WA-ANNs) for setting sediment load boundary conditions in the HEC-RAS 1D numerical model. The reconstruction performance of SRC for finding the missing sediment sampling data was at R-2 = 0.655 and NSE = 0.635. The same performance using WA-ANNs was at R-2 = 0.771 and NSE = 0.771. As the WA-ANNs have better ability to model non-linear sediment transport behavior in the Upper Indus River, the reconstructed missing suspended sediment load data were more accurate. Therefore, using more accurately-reconstructed sediment load boundary conditions in HEC-RAS, the model was better morphodynamically calibrated with R-2 = 0.980 and NSE = 0.979. Using SRC-based sediment load boundary conditions, the HEC-RAS model was calibrated with R-2 = 0.959 and NSE = 0.943. Both models validated the delta movement in the Tarbela Reservoir with R-2 = 0.968, NSE = 0.959 and R-2 = 0.950, NSE = 0.893 using WA-ANN and SRC estimates, respectively. Unlike SRC, WA-ANN-based boundary conditions provided stable simulations in HEC-RAS. In addition, WA-ANN-predicted sediment load also suggested a decrease in supply of sediment significantly to the Tarbela Reservoir in the future due to intra-annual shifting of flows from summer to pre- and post-winter. Therefore, our future predictions also suggested the stability of the sediment delta. As the WA-ANN-based sediment load boundary conditions precisely represented the physics of sediment transport, the modeling concept could very likely be used to study bed level changes in reservoirs/rivers elsewhere in the world

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

A gravel-sand bifurcation:a simple model and the stability of the equilibrium states

A river bifurcation, can be found in, for instance, a river delta, in braided or anabranching reaches, and in manmade side channels in restored river reaches. Depending on the partitioning of water and sediment over the bifurcating branches, the bifurcation develops toward (a) a stable state with two downstream branches or (b) a state in which the water discharge in one of the branches continues to increase at the expense of the other branch (Wang et al., 1995). This may lead to excessive deposition in the latter branch that eventually silts up. For navigation, flood safety, and river restoration purposes, it is important to assess and develop tools to predict such long-term behavior of the bifurcation. A first and highly schematized one-dimensional model describing (the development towards) the equilibrium states of two bifurcating branches was developed by Wang et al (1995). The use of a one-dimensional model implies the need for a nodal point relation that describes the partitioning of sediment over the bifurcating branches. Wang et al (1995) introduce a nodal point relation as a function of the partitioning of the water discharge. They simplify their nodal point relation to the following form: s*=q*k , where s* denotes the ratio of the sediment discharges per unit width in the bifurcating branches, q* denotes the ratio of the water discharges per unit width in the bifurcating branches, and k is a constant. The Wang et al. (1995) model is limited to conditions with unisize sediment and application of the Engelund & Hansen (1967) sediment transport relation. They assume the same constant base level for the two bifurcating branches, and constant water and sediment discharges in the upstream channel. A mathematical stability analysis is conducted to predict the stability of the equilibrium states. Depending on the exponent k they find a stable equilibrium state with two downstream branches or a stable state with one branch only (i.e. the other branch has silted up). Here we extend the Wang et al. (1995) model to conditions with gravel and sand and study the stability of the equilibrium states

Learning to Interpret Fluid Type Phenomena via Images

Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks