A method for simultaneously reducing transducer roll bias and refraction errors in multibeam bathymetric data lacking raw data

Historical and shared multibeam bathymetric data lacking raw data (MBDLRD), resulting from early imperfections in data collection specifications or confidentiality requirements, is an important data source for regional or global digital bathymetric models (DBMs), typically stored as grids and discrete sounding sets. The ineffective reduction of transducer roll bias and refraction errors in MBDLRD can be attributed to a lack of post-processing expertise among personnel, ultimately affecting the accuracy of the DBMs. To address this issue, we propose a method that simultaneously reduces two main errors. Firstly, based on the analysis of the impact of the two main errors, multibeam swaths are segmented and reconstructed from MBDLRD. Then, two main errors are simultaneously reduced by minimizing the depth difference in the overlaps of adjacent swaths using the differential evolution algorithm. Lastly, the overlaps in grids and discrete sounding sets are processed separately using different methods. The experimental results show that the average and standard deviation of depth differences are reduced from 6.84 and 26.11 m to −0.14 and 2.33 m, respectively, after correcting the two main errors, thereby meeting the requirements of the International Hydrographic Organization special order.