Understanding the characteristics of underwater sound channels is essential for various remote sensing applications. Typically, the time-domain Green’s function or channel impulse response (CIR) is obtained using computationally intensive acoustic propagation models that rely on accurate environmental data, such as sound speed profiles and bathymetry. Ray-based blind deconvolution (RBD) offers a less computationally demanding alternative using plane-wave beamforming to estimate the Green’s function. However, the presence of noise can obscure low coherence ray arrivals, making accurate estimation challenging. This paper introduces a method using the waveguide invariant to improve the signal-to-noise ratio (SNR) of broadband Green’s functions for a moving source without prior knowledge of range. By utilizing RBD and the frequency shifts from the striation slope, we coherently combine individual Green’s functions at adjacent ranges, significantly improving the SNR. In this study, we demonstrated the proposed method via simulation and broadband noise data (200–900 Hz) collected from a moving ship in 100 m deep shallow water.

Enhancement of Green's function is accomplished from the striation pattern obtained from data. The waveguide invariant theory allows a Green's function observed at one location to be extrapolated into adjacent ranges [Song and Byun, J. Acoust. Soc. Am. 147, 2150–2158 (2020)]. Conversely, Green's functions observed at adjacent ranges can converge towards a central location for coherent combination to improve the signal-to-noise ratio (SNR). This can be accomplished from the frequency shift in the striation pattern without prior range information of each Green's functions. We demonstrate a significant improvement of SNR in the broadband Green's function from a moving ship in shallow water.