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Deepwater Alchemy
How underwater mediation has transformed deep-sea spaces
into resource-rich frontiers
Green energy technologies such as windmills, solar panels, and
electric vehicles may soon depend on material found at the seabed.
How did a space once imagined to be empty and unfathomable come to
be thought of as a treasure trove of resources? Lisa Yin Han traces
how contemporary developments in underwater sensing and imaging
materially and imaginatively transmogrify the ocean bottom into a
resource frontier capable of sustaining a digitally connected
global future.
Set against the backdrop of climate change, energy transition,
and the expansion of industrial offshore extractions, Deepwater
Alchemy looks at oceanic media and its representation of the
seabed in terms of valuable resources. From high-tech simulations
to laboratories and archives that collect and analyze sediments,
Han explores the media technologies that survey, visualize, and
condition the possibility for industrial resource extraction,
introducing the concept of extractive mediation to
describe the conflations between resource prospecting and undersea
knowledge production. Moving away from anthropocentric frameworks,
she argues that we must equalize access to deep ocean mediation and
include the submerged perspectives of multispecies communities.
From the proliferation of petroleum seismology to
environmental-impact research on seabed mining to the development
of internet-enabled seafloor observatories, Deepwater
Alchemy shows us that deepwater mediation is entangled in
existential hopes and fears for our planetary future. As the ocean
bottom becomes increasingly accessible to people, Han prompts us to
ask not whether we can tame the seafloor, but, rather, why and for
whom are we taming it?
eBook
Quantitative monitoring of the underwater environment : results of the International Marine Science and Technology Event MOQESM'14 in Brest, France
This volume constitutes the results of the International Conference on Underwater Environment, MOQESM'14, held at \"Le Quartz\" Conference Center in Brest, France, on October 14-15, 2014, within the framework of the 9th Sea Tech Week, International Marine Science and Technology Event. The objective of MOQESM'14 was to bring together researchers from both academia and industry, interested in marine robotics and hydrography with application to the coastal environment mapping and underwater infrastructures surveys. The common thread of the conference is the combination of technical control, perception, and localization, typically used in robotics, with the methods of mapping and bathymetry. The papers presented in this book focus on two main topics. Firstly, coastal and infrastructure mapping is addressed, focusing not only on hydrographic systems, but also on positioning systems, bathymetry, and remote sensing. The proposed methods rely on acoustic sensors such as side scan sonars, multibeam echo sounders, phase-measuring bathymetric sonars, as well as optical systems such as underwater laser scanners. Accurate underwater positioning is also addressed in the case of the use of a single acoustic beacon, and the latest advances in increasing the vertical precision of Global Navigation Satellite System (GNSS) are also presented. Most of the above mentioned works are closely related to autonomous marine vehicles. Consequently, the second part of the book describes some works concerning the methods associated with such type of vehicles. The selected papers focus on autonomous surface or underwater vehicles, detailing new approaches for localization, modeling, control, mapping, obstacle detection and avoidance, surfacing, and software development. Some of these works imply acoustics sensing as well as image processing. Set membership methods are also used in some papers. The applications of the work presented in this book concern in particular oceanography, monitoring of oil and gas infrastructures, and military field.
Book
Significant Wave Height Prediction with the CRBM-DBN Model
In recent years, deep learning technology has been gradually used for time series data prediction in various fields. In this paper, the restricted Boltzmann machine (RBM) in the classical deep belief network (DBN) is substituted with the conditional restricted Boltzmann machine (CRBM) containing temporal information, and the CRBM-DBN model is constructed. Key model parameters, which are determined by the particle swarm optimization (PSO) algorithm, are used to predict the significant wave height. Observed data in 2016, which are from nearshore and offshore buoys (i.e., 42020 and 42001) belonging to the National Data Buoy Center (NDBC), are taken to train the model, and the corresponding data in 2017 are used for testing with lead times of 1–24 h. In addition, we trained the data of 42040 in 2003 and tested the data in 2004 in order to investigate the prediction ability of the CRBM-DBN model for the extreme event. The prediction ability of the model is evaluated by the Nash–Sutcliffe coefficient of efficiency (CE) and root-mean-square error (RMSE). Experiments demonstrate that for the short-term (≤9 h) prediction, the RMSE and CE for the significant wave height prediction are <10 cm and >0.98, respectively. Moreover, the relative error of the short-term prediction for the maximum wave height is less than 26%. The excellent short-term and extreme events forecasting ability of the CRBM-DBN model is vital to ocean engineering applications, especially for designs of ocean structures and vessels.
Journal Article
Subsea Pipelines and Risers
Marine pipelines for the transportation of oil and gas have become a safe and reliable part of the expanding infrastructure put in place for the development of the valuable resources below the worlds seas and oceans. The design of these pipelines is a relatively new technology and continues to evolve as the design of more cost effective pipelines becomes a priority and applications move into deeper waters and more hostile environments. This updated edition of a best selling title provides the reader with a scope and depth of detail related to the design of offshore pipelines and risers not seen before in a textbook format.
eBook
Electricity from wave and tide : an introduction to marine energy
\"A concise yet technically authoritative overview of modern marine energy devices with the goal of sustainable electricity generation With 165 full-colour illustrations and photographs of devices at an advanced stage, the book provides inspiring case studies of today's most promising marine energy devices and developments, including full-scale grid-connected prototypes tested in sea conditions. It also covers the European Marine Energy Centre (EMEC) in Orkney, Scotland, where many of the devices are assessed.Topics discussed: global resources - drawing energy from the World's waves and tides history of wave and tidal stream systems theoretical background to modern developments conversion of marine energy into grid electricity modern wave energy converters and tidal stream energy converters This book is aimed at a wide readership including professionals, policy makers and employees in the energy sector needing an introduction to marine energy. Its descriptive style and technical level will also appeal to students of renewable energy, and the growing number of people who wish to understand how marine devices can contribute to carbon-free electricity generation in the 21st century\"-- Provided by publisher.
Book
Three-Dimensional Modeling of Tsunami Waves Triggered by Submarine Landslides Based on the Smoothed Particle Hydrodynamics Method
Submarine landslides are a global geohazard that can displace huge volumes of loose submarine sediment, thereby triggering enormous tsunami waves and causing a serious threat to coastal cities. To investigate the generation of submarine landslide tsunamis, a three-dimensional numerical model based on the smoothed particle hydrodynamics (SPH) method is presented in this work. The model is first validated through the simulation of two underwater landslide model tests, and is then applied to simulate the movement of the Baiyun landslide in the South China Sea (SCS). The kinetics features of the submarine landslide, including the sliding velocity and runout distance, are obtained from the SPH simulation. The tsunami waves generated by the Baiyun landslide are predicted. In addition, sensitivity analyses are conducted to investigate the impact of landslide volume and water depth on the amplitude of the tsunami waves. The results indicate that the amplitude of tsunami waves triggered by submarine landslides increases with the landslide volume and decreases with the water depth of the landslide.
Journal Article
Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network
Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significant wave height forecasting, a method widely used in wind speed forecasting, but not yet for wave heights. Following a comparative analysis, the results demonstrate that EMD-LSTM significantly outperforms LSTM at every forecast horizon (3, 6, 12, 24, 48, and 72 h), considerably improving forecasting accuracy, especially for forecasts exceeding 24 h. Additionally, EMD-LSTM responds faster than LSTM to large waves. An error analysis comparing LSTM and EMD-LSTM demonstrates that LSTM errors are more systematic. This study also identifies that LSTM is not able to adequately predict high-frequency significant wave height intrinsic mode functions, which leaves room for further improvements.
Journal Article