Sound velocity errors often lead to distortion of seafloor topography in multibeam bathymetric data processing. The current method of sound velocity profile (SVP) inversion has the problems of small application scope of the inversed equivalent sound velocity profile (ESVP) and terrain distortion calculated by inversed SVP under the condition of water depth consistency constraint in overlapping line area. In this paper, we propose a method of SVP inversion using the Empirical Orthogonal Function (EOF) and Differential Evolution (DE) algorithm. Firstly, EOF is used to decompose the SVP matrix, then the water depth discrepancy between the check line and the main survey line is used as the objective function instead of the discrepancy of overlapping line area, and DE is used to find the optimal time coefficient, and finally, the SVP is reconstructed to correct the seabed topography. The method was verified by experiments in shallow and mid-deep water, and the mean absolute and standard deviation of the corrected bathymetric discrepancy values are about 50% of those before correction.

The presence of sound velocity errors (SVEs) is common in multibeam sounding. Existing methods are good at eliminating these errors in the area with flatter seabed terrains but are not suitable for deep and complex seabed terrains. Therefore, a universal method is proposed in this paper to eliminate these errors in various depth and terrain areas with a regression model and the equivalent sound speed profile (ESSP). The vertical biases are calculated at these homonymy points of the main survey line and the inner swaths of the auxiliary survey line firstly. Then, a regression model is built by associating the vertical bias with the initial incident angle and travel time, and the regression coefficients are estimated with the help of the partial least squares (PLS) method. On the basis of the determined regression model and ESSP, the effects of sound errors for multibeam sounding data are eliminated. The proposed method was verified by the multibeam sounding data measured in the medium-shallow and deep water areas, and the standard deviations of the absolute values of depth differences (AVDDs) after correction were both about 2 times lower than those before correction.

Multibeam echosounder systems have been becoming a standard acoustic remote sensing approach and are widely applied in ocean mapping tasks. The errors of lever arm between heave sensor and transducer due to deformation or mistaken measurement will be manifested as ship-track orthogonal bathymetric undulations, which will hinder the accurate expression of high-resolution seafloor topographic maps. To address this issue, in this article, the characteristics of lever arm errors and the distinction from other errors are analyzed, and a new method is proposed to calibrate the lever arm errors. First, the periodical pattern of abnormal undulations caused by lever arm errors is extracted by the Two-Way Difference Method and Multistart Algorithm. Then, the correction model is established and the Random Sample Consensus Algorithm with moving windows is used to obtain the correction water depth. Finally, support vector regression is used to calculate the lever arm errors with the correction depth and initial depth. The results indicate that the proposed method can significantly eliminate ship-track orthogonal bathymetric undulations in multibeam bathymetry caused by lever arm errors and is robust in complex seabed topography.

Horizon picking underpins the interpretation of subbottom profile (SBP) data, acoustical inversion, and related applications. We present a horizon-picking method based on an improved wavelet multiresolution analysis. The approach uses low-resolution images from multiscale decomposition to track lateral layer continuity and combines nonmaximum suppression with hysteresis thresholding to achieve uninterrupted detection. A graph-based linking algorithm further refines edges and improves accuracy. To correct horizontal offsets and localization errors caused by phase distortion during wavelet reconstruction, we introduce a morphology-guided reconstruction strategy. Key parameters are determined in synthetic experiments and validated on field data. Compared with traditional approaches, the proposed method yields more continuous, accurate, and high-resolution horizons. The method’s effectiveness is demonstrated on SBP data acquired in Jiaozhou Bay.