Improving the Ability of Measuring the Thickness of Thin Oil Slick with Ultrasound Using the Sparse Optimization Method

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.