# Topophone2

Array processing has long treated inter-sensor coherence loss as a nuisance, and conventional beamforming assumes a far-field point source with incoherent noise between sensors. Topophone2 models the array covariance as a mixture of energy distributions over slowness, describing signal and environmental noise with skew-normal and angular-harmonic distributions that have analytical characteristic functions, then computes the posterior by variational Bayesian inference. On a synthetic abyssal T-phase, adding a fourth hydrophone raises the information gain on the angular-harmonic noise coefficients by 142%, while the gain on signal parameters and SNR rises by less than 8%.

Array processing has long treated inter-sensor coherence loss as a nuisance, and conventional beamforming assumes a far-field point source with incoherent noise between sensors. Topophone2 models the array covariance as a mixture of energy distributions over slowness, describing signal and environmental noise with skew-normal and angular-harmonic distributions that have analytical characteristic functions, then computes the posterior by variational Bayesian inference. On a synthetic abyssal T-phase, adding a fourth hydrophone raises the information gain on the angular-harmonic noise coefficients by 142%, while the gain on signal parameters and SNR rises by less than 8%.