Abstract
Distributed acoustic sensing (DAS) systems are increasingly employed for passive detection and localization in geophysical and underwater acoustic applications. However, fiber-optic sensing presents several challenges related to strain-based measurements, sensitivity, and angular response, limiting the direct application of conventional array processing methods. Uncertainty in propagation speeds, complex dispersive propagation, and imperfect knowledge ofcable position further complicate accurate source localization. To address these challenges, this study considers coherent combining techniques within a hierarchical Bayesian localization framework tailored to DAS arrays. Localization approaches are developed for an impulsive seismic source on the Earth’s surface in the near field of a DAS array, as well as for passive underwater acoustic sources. Marginalization over the weakly known source disturbance yields a posterior probability density of the source location. For the terrestrial case, the forward model incorporates a near-field Green’s function with surface-wave dispersion appropriate for layered media and accounts for the DAS angular responseto surface waves. Modal information is extracted using frequency–wavenumber analysis and Radon transform, and empirical dispersion curves are fitted with a two-term exponential model. Application to DAS observations showspromising localization accuracy.