Objectives  Currently , the measured sound velocity profile with high accuracy is lack in the processing of the crowd sourced bathymetry(CSB) data, so that the quality of the sounding data is low. Aiming at this situation above, a method for utilizing the inversion sound velocity profile obtained from the BP neural network model optimized by the genetic algorithm (GA-NN model) is proposed to correct the CSB data.

  Methods  Firstly, the eigenvector and the reconstruction coefficient range are extracted from the results via empirical orthogonal functions analysis of the historical sound velocity profile group. Secondly, GA-NN model is trained by utilizing the information of the historical sound velocity profile field. Finally, the surface sound velocity is inputted into the model for getting the inversion sound velocity profile, and statistically analyzed the depth and position errors after correcting the data via different sound velocity profiles.

  Results  The experimental results show that in the sea area with complex seafloor topography, the error index of sound velocity profile inverted by the proposed method is smaller, the corrected seabed topography is more fitting to the simulated seabed, and the error of water depth and position is smaller.

  Conclusions  The sound velocity profile inverted by the proposed method is more suitable for the sound speed correction in the CSB work, weaks the influence of sound velocity error on seafloor topography, and improves the precision of CSB data.