We examine the effect of noise on signature estimation via least-squares inversion of near-field data. We look at the case of solving for real and virtual notional sources using twice the standard number of hydrophones within the source array. This approach has the advantage of being parameter-free as it removes assumptions on the magnitude of the sea-surface ghost and its period, but we find it is more sensitive to the presence of noise. This is due to the greater distances from the points of measurement to the unknown virtual notional sources when compared with distance to the real notional sources. We present results on field data for a hybrid methodology that utilizes the parameter-free approach at high frequency and a standard ghost model at low-frequency. We show that this approach ensures a good representation of both the ghost and the bubble oscillation.


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