A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea

Abstract

In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.Remote Sens. 2015, 7 7106

Cyber-physical oil spill monitoring and detection for offshore petroleum risk management service

“…A large number of researches attempt to accurately detect oil spill in ASAR 32 , 33 . Thus, in this paper, a single threshold segmentation method, proposed by Yan, et al in 2015 18 , is utilized to extract oil leak region.…”

“…The PSO method is a widely-used evolutionary method which has been widely applied in resources allocation, susceptibility analysis and intelligent diagnosis [34][35][36] . Besides, the Dynamic remote sensing Data-Driven Application System (DDDAS) based oil spill detection approach is also utilized as a comparison method since this technique is also targeting to solve the oil spill problems in Bohai sea 18 . To illustrate the efficiency of the proposed parallel method, a serial implementation of the proposed method is also execute on a single compute node of the super computer.…”

“…It was later improved to the MEDSLIK-II Lagrangian marine model which can be utilized to simulate the spread and transformation processes of leaking oil by using both SAR (synthetic aperture radar) data and optical image data 17 . In 18 , an Estuarine Coastal Ocean Model (ECOM-3d) was established by combining a remote sensor system that detects oil spills with a numerical simulation based on a dynamic data-driven application system. These existing techniques suffer from insufficient remote sensing resources and expensive computational cost, when working with realistic scenarios.…”

Offshore Petroleum Leaking Source Detection Method From Remote Sensing Data via Deep Reinforcement Learning With Knowledge Transfer

“…Later, the MEDSLIK oil spill model was improved in the MEDSLIK-II Lagrangian model, which combined SAR and optical image data to simulate the oil slick diffusion and transformation processes [27]. Based on remote-sensing data for marine oil spill monitoring and driven by dynamic remote-sensing data, Yan et al [28] used a back propagation neural network to find oil spills. This method has certain limitations; the identification of an oil spill's location depends on experience or related accurate news reports in the initial simulation of the source.…”

“…After a series of preprocessing, including geometric correction, coordinate transformation, etc., the Support Vector Machine (SVM) method extracts the oil leak region from the remote-sensing data. Note that the geographic coordinate system is transformed into WGS84, and the geometric precision correction and an enhanced Lee filter are applied for ASAR data process [34] [35] [28]. The oil film is then discretized to a large number of points based on the remote-sensing data.…”