Unsupervised super-spatial-resolution Brillouin frequency shift extraction based on physical enhanced spatial resolution neural network

Spatial resolution (SR), a core parameter of Brillouin optical time-domain analysis (BOTDA) sensors, determines the minimum fiber length over which physical perturbations can be accurately detected. However, the phonon lifetime in the fiber imposes an inherent limit on the SR, making sub-meter-level SR challenging in high-SR monitoring scenarios. Conventional SR enhancement approaches, constrained by hardware limitations, often involve complex systems, or increased measurement times. Although traditional deconvolution methods can mitigate hardware constraints, they suffer from distortion due to the nonlinear nature of the BOTDA response. Supervised deep learning approaches have recently emerged as an alternative, offering faster and more accurate post-processing through data-driven models. However, the need for extensive labeled data and the lack of physical priors lead to high computational costs and limited generalization. To overcome these challenges, we propose an unsupervised deep learning deconvolution framework, Physics-enhanced SR deep neural network (PSRN) guided by an approximate convolution model of the Brillouin gain spectrum (BGS).