An Automatic Method for Layer Boundary Extraction from Sub-Bottom Profile Images

Accurate delineation of seabed sedimentary structures is vital for marine engineering and exploration. Sub-bottom profilers (SBPs) provide a cost-effective means of sub-surface imaging; however, their utility is often hampered by noise, multiple reflections, and data inconsistencies. This study introduces an automated, high-precision algorithm for extracting layer boundaries from SBP images. The algorithm employs a three-step computational framework: (1) adaptive preprocessing involving SEG-Y decoding, image reconstruction, and hybrid noise reduction using the Maximum Inter-class Distance method; (2) gradient-based edge detection utilizing localized statistical windows and dynamic thresholding techniques; and (3) topology-aware refinement through connectivity tracking, neighborhood merging, and false boundary elimination. Sea trial results demonstrate the robustness of the proposed method, achieving enhanced continuity and precision in automated interpretation, thus providing a reliable tool for marine geological structure analysis.