Multibeam echosounder (MBES) backscatter data are influenced by underwater sound absorption, which is dependent on environmental parameters such as temperature, salinity, and depth. This study leverages CTD datasets from the Korea Oceanographic Data Center (KODC) to analyze and visualize the spatiotemporal variations in absorption parameters in the East Sea of Korea, which are subject to pronounced variability over time and space. The legacy MBES backscatter data, originally processed using generalized absorption parameters that neglected spatiotemporal variations, were compared with the calibrated data. The calibration process included inverse calculation of water temperature with depth-specific average salinity values from the nearest KODC stations. This calibration revealed discrepancies of up to 2.1 dB in backscatter intensity across survey lines, highlighting the potential misrepresentation of legacy MBES backscatter data due to site-specific absorption variability having been overlooked. By addressing these discrepancies, this study underscores the importance of incorporating spatiotemporal absorption variability into MBES calibration workflows. This integrated approach not only enhances the reliability of legacy MBES data but also provides valuable insights for marine resource management, seafloor mapping, and environmental monitoring in highly dynamic marine environments such as the East Sea of Korea.

Seabed-sediment classification from multibeam echo-sounder data is constrained by sparse, class-imbalanced ground truth, particularly for coarse gravel, which pixel-based classifiers struggle to resolve. This study maps the seabed sediments of the southwestern East Sea (Sea of Japan) continental shelf using object-based sample augmentation, which propagates field-sample labels across Simple Linear Iterative Clustering superpixels of calibrated backscatter and bathymetry. Under 20-seed repeated random cross-validation, object-based augmentation raised the rare-class (Gravel) F1-score from 0.036 for a pixel-only baseline to 0.436, and the gain persisted under spatial-block cross-validation, a within-scene spatial-robustness test on spatially held-out sites (0.024 to 0.311), where SLIC exceeded all tested fixed-neighborhood scales. These rare-class estimates rest on only eight independent Gravel field sites and should be interpreted in that context. Oversampling the original rare-class anchors to the same training size recovered only a minority of this gain, indicating that most of the improvement arose from newly propagated pixels rather than anchor replication. Feature-space and multi-sample diagnostics provided empirical support for the within-superpixel label-propagation assumption. The resulting gravel–sand–mud map reproduces a depth-controlled, fining-offshore facies pattern consistent with hydraulic sorting on the narrow, river-fed shelf. An expert-interpreted Rock assessment further showed that exposed bedrock could not be reliably separated from coarse gravel at an acceptable trade-off in the available single-frequency-per-location backscatter–bathymetry feature space. Overall, the approach recovers rare coarse-substrate facies that pixel-based mapping largely misses while identifying the limits of acoustic-only substrate discrimination, thereby strengthening the baseline seabed-sediment maps that underpin benthic habitat mapping, marine spatial planning, and seabed engineering.