A Method for Streamlining and Assessing Sound Velocity Profiles Based on MOV Algorithm

A multi-beam echosounder is a type of sonar that is used to map the water bottom. Like other sonar systems, multi-beam systems emit sound waves in a fan shape beneath a ship's hull and receives the round-trip time of their reflection or scattering, and thus it is possible to determine the depth and coordinates of the detected targets using the sound velocity profile (SVP) based on Snell’s Law. The SVP is determined by a device. Because of the high sampling rate of the modern device, the operational time of ray tracing and beam footprint reduction will increase, lowering the overall efficiency. To promote the timeliness of multi-beam surveys and data processing, redundant points in the original SVP must be screened out and at the same time, errors following the streamlining of the SVP must be evaluated and controlled.

We presents a new streamlining and evaluation method based on the Maximum Offset of sound Velocity (MOV) algorithm. Based on measured SVP data, this method selects sound velocity data points by calculating the maximum distance to the sound-velocity-dimension based on an improved Douglas-Peucker Algorithm to streamline the SVP (Fig. 1). To evaluate whether the streamlined SVP meets the desired accuracy requirements, this method is divided into two parts: SVP streamlining, and an accuracy analysis of the multi-beam sounding data processing using the streamlined SVP. Therefore, the method is divided into two modules: the streamlining module and the evaluation module (Fig. 2). The streamlining module is used for streamlining the SVP. Its core is the MOV algorithm.To assess the accuracy of the streamlined SVP, we uses ray tracing and the percentage error analysis method to evaluate the accuracy of the sounding data both before and after streamlining the SVP (Fig. 3). By automatically optimizing the threshold, the reduction rate of sound velocity profile data can reach over 90% and the standard deviation percentage error of sounding data can be controlled to within 0.1% (Fig. 4). The optimized sound velocity profile data improved the operational efficiency of the multi-beam survey and data post-processing by 3.4 times(Fig. 5), indicating that this algorithm has practical value for engineering applications.