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Petroleum industry has started to embrace the advanced petroleum cyber-physical system (CPS) technologies. Offshore petroleum CPS is particularly hard to build, mainly due to the difficulty in detecting and preventing offshore oil leaking. During the oil exploration and transportation process, the remote multi-sensing technology is typically employed for emerging service. It can be utilized for leak detection by enabling the underwater modeling of an offshore petroleum CPS. However, such a technology suffers from insufficient remote sensing resources and expensive computational overhead. In this work, a cross-entropy based leak detection technique is proposed to detect the oil leak, which facilitates the understanding of the oil leak induced marine pollution. Furthermore, a hierarchical parallel approach is proposed on the super computer Tianhe-2 to improve the efficiency of the proposed leak detection technique. Experimental results on Penglai oil spill events demonstrate that the proposed method can effectively identify the sources of oil spilling with accuracy up to $$100\%$$ 100 % .

A marine oil spill is an environmental pollution incident that generally has the attributes of a high speed, widespread, and long duration. It seriously threatens the marine ecological environment and related industries. It is vital to determine the source of the oil leakage so that it may be stopped and related hazards can be reduced. Oil spill accidents in the sea are generally located in offshore and navigation channels. With the rapid development of remote-sensing techniques, oil leak extraction using remote-sensing data has played an essential role in oil spill research. This paper proposes a Monte Carlobased Deep Q-Transfer-learning Network (DQTN) offshore oil leak detection method that uses remote-sensing data. Remotesensing data are utilized to continuously monitor a marine oil spill on the surface. The Estuarine and Coastal Ocean Model (ECOM) is utilized to simulate a marine oil spill event. The Deep Q Network (DQN) method with offline transferred knowledge is then utilized to determine the marine oil spill source location. In an experiment, based on the Bohai oil spill incident on June 2, 2011, the effectiveness of the remote-sensing-based DQTN marine oil spill search algorithm is verified. The accuracy of the targeted oil spill point is up to 98.97%.

Abstract. Storm surge inundation hazards annually cause billions in economic losses globally, and millions of coastal residents live in danger. Properly understanding and assessing storm surge inundation are essential measures to guarantee the sustainable construction of coastal cities. In this paper, a differentiated urban risk semi-quantitative assessment method for storm surge inundation is proposed to evaluate the risk of storm surge hazard causing inundation to the coastal city. The Finite Volume Community Ocean Model (FVCOM) and the Jelesnianski model restore the historical storm surge cases to reveal hazards. The point of interest data and the urban land use and land cover data are utilized to assess the vulnerability of the coastal city, and a differentiated risk assessment method is proposed to evaluate the risks for urban facilities in the hazard. As an illustration, the method is utilized to assess storm surge Mangkhut in 2018 in Shenzhen, Guangdong Province, China. The vulnerability of the Shenzhen downtown area is assessed and designed as a map to visualize the strategic area. According to numerical simulation and inundation region mapping, the danger and the risk assessment map are made to intuitively present the distribution of the hazard-affected region.

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