Data Feedback Correction: A Method for Eliminating Heave Residuals in Shallow-Water Multibeam Bathymetry

The accuracy of shallow-water multibeam bathymetry is critically dependent on precise heave correction. However, sensor limitations often lead to incomplete correction, leaving periodic along-track stripe noises (heave residuals) that distort seabed morphology. Traditional filtering methods suppress this noise at the expense of genuine topographic detail. This paper proposes an innovative Data Feedback Correction (DFC) method that corrects the error at its source. DFC establishes a closed-loop framework: it diagnoses the residual’s dominant frequency from central beam data, extracts the residual signal via targeted filtering, and feeds it back as a compensation term into the original sensor heave sequence. This drives a recomputation of the geometric positioning, achieving source-level correction. In a field case, DFC demonstrated targeted, high-fidelity performance. Across all 34 survey lines, DFC achieved an average spectral attenuation of 1.85 dB (range: 1.0–3.7 dB) in the dominant residual band and reduced the RMSE of overlap discrepancies from 0.0923 m to 0.0773 m (a 16.25% improvement). Independent validation using 94,999 control line intersections further demonstrates a 14.31% RMSE improvement relative to an uncorrected control line reference, confirming that the correction improves both internal consistency and external accuracy, significantly enhancing internal consistency. Compared to moving average and wavelet denoising, DFC achieved comparable quantitative improvement while effectively suppressing visual stripes and features that are consistent with the original data, avoiding the over-smoothing or residual noise of traditional methods. This study confirms that closed-loop feedback of data residuals can fundamentally address spectrally aliased stripe noise, shifting the paradigm from “masking noise” to “correcting the source.” The method enhances data consistency in the tested scenario without sacrificing topographic authenticity, providing a promising new tool that warrants further validation across diverse survey conditions.