Using a library of shipping sources for data-driven localization of nearby sources

Underwater source localization is often achieved with a purely model-based approach such as matched-field processing with simulated replica-field. However, such approaches only yield reasonable predictions if the complex and dynamic ocean environment is sufficiently known – often a daunting task. Alternatively, it has been suggested that channel impulse responses (CIRs) estimated from measurements of sources of opportunity (such as commercial shipping vessels) can feed a data-driven approach to source localization that forgoes the need for precise model-parameters [Durofchalk et al., JASA 146(4), 2691–2691 (2019)]. In this presentation, multiple vertical line array (VLA) data from the SBCEx16 experiment conducted in the vicinity of shipping lanes in the Santa Barbara channel (580 m depth, downward refracting profile) are first used to a construct a library of estimated CIRs between selected locations along opportunistic shipping tracks and VLA receivers using ray-based blind deconvolution (RBD) [Byun et al., JASA 141(2), 797–807 (2017)]. Subsequently, this library of data derived CIRs is used to localize other surface sources with traditional matched-field processing techniques and as training data for a machine learning algorithm. The average localization error and computational efficiency of the different methods are compared.