In the context of deep-water ocean exploration, sub-bottom profiling and multibeam bathymetric surveys are frequently integrated and synchronised to facilitate the concurrent acquisition of deep-water seafloor topography and fine sedimentary stratigraphic data. However, the complex topographic changes present in the deep ocean pose significant challenges in the exploration of the accuracy of deep-sea sub-bottom profiles. These challenges include the real-time tracking of the seafloor reflector and the splicing of profiles derived from deep-sea long-term reflection data. The accurate determination of the seafloor reflecting depth is a prerequisite for the effective correction of the data volume of deep-sea sub-bottom profiles. The development of a precise positioning and noise suppression method, namely the nearest position pair and the inverse distance weighted interpolation algorithm, has enabled the correction of the seafloor’s reflecting depth and the effective splicing of sub-bottom profile data in deep water. The enhancement of the quality of deep-water sub-bottom profile data is facilitated by these processing techniques, thereby enabling the provision of a comprehensive and successively long profile for interpretation in the context of deep-water sub-bottom profile data.

The complex topography of deep sea presents numerous challenges for the accurate exploration of sub-bottom profiles. These include real-time tracking of seafloor reflectors, acquisition and storage of deep-sea long-term reflection data, and splicing of successive profiles. Based on the actual survey data of deep sea, we have developed automatic positioning and noise suppression algorithms, namely the double-difference threshold of proximity points. Furthermore, we have created automatic algorithms, namely content expansion and group data moving, based on extremum in seafloor’s depth. These have been designed to automatically suppress the random noise and effectively splice the sub-bottom profile data in deep water. The aforementioned processing techniques facilitate the enhancement of the quality of deep-water sub-bottom profile data, thereby enabling the provision of a comprehensive and successively long profile for interpretation in the context of deep-water sub-bottom profile data.