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

Abstract

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 article proposes a Monte Carlo-based deep Q-transfer-learning network (DQTN) offshore oil leak detection method that uses remote-sensing data. Remote-sensing data are utilized to continuously monitor a marine oil spill on the surface. The estuarine and coastal ocean model is utilized to simulate a marine oil spill event. The deep-Q-network 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%.

Keywords

• Deep Q-transfer-learning network (DQTN)
• estuarine and coastal ocean model (ECOM)
• Monte Carlo
• oil spill detection
• remote sensing