An ultrasonic method was proposed to solve the problem of accurately measuring the thickness of oil slick using a Remotely Operated Vehicle (ROV) as a platform. The laboratory experiments were performed to collect ultrasonic reflections from interfaces underneath oil slick during ROV’s resting and running. A new signal processing method was proposed to improve the accuracy of the thickness measurement by precisely calculating the transmit time of flight (TOF) of ultrasound within oil slick. The feasibility of this method was verified by performing eight measurements of oil slick thicknesses during ROV’s resting and running. The results showed that the average values of absolute differences between ROV’s resting and running were both less than 4.0% for two transducers, a result which indicated the high measurement accuracy, repeatability and stability of this method. In addition, the minimum measurable thickness for each transducer was initially investigated, and the suitable transducer frequency was also determined. The ultrasonic method proposed in this paper demonstrates a strong capability of measuring the thickness of thin oil slick with the high accuracy. The studies in this paper would promote the development and application of this method in the oil spill response. It could possibly be developed into an applicable tool for solving the problem of measuring the thickness of oil in the field in the future.

During oil spills, using ultrasound to measure the thickness of thick oil slick can provide critical information for determining the oil spill response strategy and improving the response efficiency. However, the ultrasonic measuring ability is strictly limited by the wavelength of ultrasound. To expand the ability to measure a thin oil slick thickness, a super-resolution algorithm based on a generalization of the sparse iterative covariance estimation (SPICE) algorithm was proposed to calculate the time of flight (TOF) by processing ultrasonic data collected with two transducers installed on a remotely operative vehicle (ROV). Firstly, the cross-correlation of ultrasonic signals was performed to improve the signal-to-noise ratio (SNR). Then, the outputs were processed with the inverse discrete Fourier transform (IDFT) to directly generate snapshot samples. At last, a sparse iterative covariance-based algorithm taking account of the signal sparseness was developed to deal with snapshot samples to achieve the TOF. The simulations were conducted to compare the proposed method with the generalized cross-correlation (GCC) method and the Modified Orthogonal Matching Pursuit (MOMP) method. The experimental results show that the proposed method can significantly improve the ability to measure thin oil slick thickness by almost halving the minimum measurable thickness with superior measurement accuracy in comparison to the GCC method. It could be applied to measure the thickness of an oil slick during an oil spill response.

A super-resolution algorithm is proposed to solve the problem of measuring the thin thickness of oil slick using compressed sensing theory. First, a mathematical model of a single pulse underwater ultrasonic echo is established. Then, the estimation model of the transmit time of flight (TOF) of ultrasonic echo within oil slick is given based on the sparsity of echo signals. At last, the super-resolution TOF value can be obtained by solving the sparse convex optimization problem. Simulations and experiments are conducted to validate the performance of the proposed method.