A Transformer Network for Phase Unwrapping in Fiber-Optic Acoustic Sensors

Fiber-optic acoustic sensors, such as interferometric fiber-optic sensor array and phase-sensitive optical time-domain reflectometry, have excellent sensitivity and have been widely adopted in applications. However, their sampling rates are limited by the round-trip time of laser in the sensor array or the sensing fiber, which finally restricts the dynamic range due to the issue of phase wrapping. Classical phase unwrapping function requires the signal to obey the Itoh condition, otherwise, the recovery of true phase for strongly-swinging signals will fail. In this work, we propose a neural network for phase unwrapping of interferometric sensing signals named Prearranged Ascending Receptive Field Transformer (PARFT). The Transformer architecture with regressive output was employed which is capable of time-domain signal processing, and 1D convolution layers with ascending kernel sizes were designed to replace the positional encoding in standard Transformer, providing particular inductive biases aiming at the phase unwrapping problem. For both simulated dataset via random matrix enlargement (RME) method and real dataset recorded by distributed acoustic sensor (DAS), the network showed high efficiency and competitive accuracy in phase-unwrapping of signals violating the Itoh condition, compared with previously proposed neural networks or handcrafted algorithms.