ALBERT

Description: Deepwater spills pose a unique challenge for reliable predictions of oil transport and fate, since live oil spewing under very high hydrostatic pressure has characteristics remarkably distinct from oil spilling in shallow water. It is thus important to describe in detail the complex thermodynamic processes occurring in the near-field, meters above the wellhead, and the hydrodynamic processes in the far-field, up to kilometers away. However, these processes are typically modeled separately since they occur at different scales. Here we directly couple two oil prediction applications developed during the Deepwater Horizon blowout operating at different scales: the near-field Texas A&M Oilspill Calculator (TAMOC) and the far-field oil application of the Connectivity Modeling System (oil-CMS). To achieve this coupling, new oil-CMS modules were developed to read TAMOC output, which consists of the description of distinct oil droplet “types,” each of specific size and pseudo-component mixture that enters at a given mass flow rate, time, and position into the far field. These variables are transformed for use in the individual-based framework of CMS, where each droplet type fits into a droplet size distribution (DSD). Here we used 19 pseudo-components representing a large range of hydrocarbon compounds and their respective thermodynamic properties. Simulation results show that the dispersion pathway of the different droplet types varies significantly. Indeed, some droplet types remain suspended in the subsea over months, while others accumulate in the surface layers. In addition, the decay rate of oil pseudo-components significantly alters the dispersion, denoting the importance of more biodegradation and dissolution studies of chemically and naturally dispersed live oil at high pressure. This new modeling tool shows the potential for improved accuracy in predictions of oil partition in the water column and of advancing impact assessment and response during a deepwater spill.

Description: Petroleum is one of the most complex naturally occurring organic mixtures. The physical and chemical properties of petroleum in a reservoir depend on its molecular composition and the reservoir conditions (temperature, pressure). The composition of petroleum varies greatly, ranging from the simplest gas (methane), condensates, conventional crude oil to heavy oil and oil sands bitumen with complex molecules having molecular weights in excess of 1000 daltons (Da). The distribution of petroleum constituents in a reservoir largely depends on source facies (original organic material buried), age (evolution of organisms), depositional environment (dysoxic versus anoxic), maturity of the source rock (kerogen) at time of expulsion, primary/secondary migration, and in-reservoir alteration such as biodegradation, gas washing, water washing, segregation, and/or mixing from different oil charges. These geochemical aspects define the physical characteristics of a petroleum in the reservoir, including its density and viscosity. When the petroleum is released from the reservoir through an oil exploration accident like in the case of the Deepwater Horizon event, several processes are affecting the physical and chemical properties of the petroleum from the well head into the deep sea. A better understanding of these properties is crucial for the development of near-field oil spill models, oil droplet and gas bubble calculations, and partitioning behavior of oil components in the water. Section 3.1 introduces general aspects of the origin of petroleum, the impact of geochemical processes on the composition of a petroleum, and some molecular compositional and physicochemical background information of the Macondo well oil. Section 3.2 gives an overview over experimental determination of all relevant physicochemical properties of petroleum, especially of petroleum under reservoir conditions. Based on the phase equilibrium modeling using equations of state (EOS), a number of these properties can be predicted which is presented in Sect. 3.3 along with a comparison to experimental data obtained with methods described in Sect. 3.2.

Description: Landslide is one of the dangerous types of natural hazards. This phenomenon causes damages in many countries every year. A detailed landslide hazard assessment is necessary to reduce these damages. This research aims to map the landslide susceptibility zoning (LSZ) using the fuzzy logic method and GIS in the Sorkhab basin as a part of the Zagros fold and thrust belt (FTB), northwestern Iran. All slide types were recorded in fieldwork as landslide inventory. Based on the results, four types, i.e., debris slide, earth slide, and rock fall and complex of landslides, was identified in the region. Then, the effect of each landslide contributing factor including topographical elevation heights, slope classes, aspect classes, geological units, proximity to faults, land covers, rainfall classes, and proximity to streams was constructed in GIS and subsequently normalized using fuzzy membership functions. Finally, by combining all standardized layers using the fuzzy gamma operator, a final map of LSZ was produced. The results showed that a 0.9 fuzzy gamma operator has a high accuracy for the LSZ map in the study area. Besides, the accuracy of the LSZ map revealed a strong relationship (R2) between susceptibility classes, and landslide inventory was calculated using a scatter plot equal to 0.79. Hence, the method represented an appropriate accuracy in predicting the landslide susceptibility in the study area.

Description: Landslide deposits offshore many volcanic islands provide evidence of catastrophic lateral collapses. These deposits span a larger volume range than their continental equivalents, and can generate devastating tsunamis. All historical volcanic-island lateral collapses have occurred in arc settings, and have been characterised by rapid failure and efficient tsunami generation. The varied morphology of their deposits is influenced both by lithological properties and the nature of the substrate. Many deposits show evidence of extensive seafloor erosion and transformation into debris flows, and the propagation of frontally-confined sediment deformation beyond and beneath the primary deposit. Mobilised volumes can far exceed that of the initial failure, and accurate deposit interpretation requires internal geophysical imaging and sampling. Around intraplate ocean-island volcanoes, multi-unit turbidites suggest that lateral collapses may occur in discrete stages; although this would reduce their overall tsunamigenic potential, the volumes of individual stages of collapse remain very large. Numerical models of both landslide and tsunami processes in ocean-island settings are difficult to test, and the smaller collapses that typify island arcs are an important focus of research due to their higher global frequency, availability of direct failure and tsunami observations, and a need to better understand the signals of incipient collapse to develop approaches for tsunami hazard mitigation.

Description: Since the sunlight only penetrates a few hundred meters into the ocean, deep-diving robots have to bring their own light sources for imaging the deep sea, e.g., to inspect hydrothermal vent fields. Such co-moving light sources mounted not very far from a camera introduce uneven illumination and dynamic patterns on seafloor structures but also illuminate particles in the water column and create scattered light in the illuminated volume in front of the camera. In this scenario, a key challenge for forward-looking robots inspecting vertical structures in complex terrain is to identify free space (water) for navigation. At the same time, visual SLAM and 3D reconstruction algorithms should only map rigid structures, but not get distracted by apparent patterns in the water, which often resulted in very noisy maps or 3D models with many artefacts. Both challenges, free space detection, and clean mapping could benefit from pre-segmenting the images before maneuvering or 3D reconstruction. We derive a training scheme that exploits depth maps of a reconstructed 3D model of a black smoker field in 1400 m water depth, resulting in a carefully selected, ground-truthed data set of 1000 images. Using this set, we compare the advantages and drawbacks of a classical Markov Random Field-based segmentation solution (graph cut) and a deep learning-based scheme (U-Net) to finding free space in forward-looking cameras in the deep ocean.

Description: Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator).

Description: In deep water conditions, vision systems mounted on underwater robotic platforms require artificial light sources to illuminate the scene. The particular lighting configurations significantly influence the quality of the captured underwater images and can make their analysis much harder or easier. Nowadays, classical monolithic Xenon flashes are gradually being replaced by more flexible setups of multiple powerful LEDs. However, this raises the question of how to arrange these light sources, given different types of seawater and-depending-on different flying altitudes of the capture platforms. Hence, this paper presents a rendering based coarse-to-fine approach to optimize recent multi-light setups for underwater vehicles. It uses physical underwater light transport models and target ocean and mission parameters to simulate the underwater images as would be observed by a camera system with particular lighting setups. This paper proposes to systematically vary certain design parameters such as each LED’s orientation and analyses the rendered image properties (such as illuminated image area and light uniformity) to find optimal light configurations. We report first results on a real, ongoing AUV light design process for deep sea mission conditions.

Description: The Trans-European Suture Zone (TESZ) is the transition zone from the Precambrian East European Craton in the north and east to the younger Phanerozoic mobile belts to the south and west. It is the most prominent lithospheric tectonic feature of Europe. The term Trans-European Suture Zone was only adapted around year 2000 during the Pan-European EUROPROBE program of the European Science Foundation. Until then, parts of the zone were termed Teisseyre-Tornquist Zone, Sorgenfrei-Tornquist Zone, Trans-European Fault, and Tornquist Fan.

Description: The COVID-19 pandemic necessitates a change in conference formats for 2020. This shift offers a unique opportunity to address long-standing inequities in access and issues of sustainability associated with traditional conference formats, through testing online platforms. However, moving online is not a panacea for all of these concerns, particularly those arising from uneven distribution of access to the Internet and other technology. With conferences and events being forced to move online, this is a critical juncture to examine how online formats can be used to best effect and to reduce the inequities of in-person meetings. In this article, we highlight that a thoughtful and equitable move to online formats could vastly strengthen the global socio-ecological research community and foster cohesive and effective collaborations, with ecology and society being the ultimate beneficiaries.