Deep-Learning-based Spatial Acoustic Properties Recovery from Incomplete Signals

We propose a physics-informed neural network (PINN) based approach that can recover the spatially-varying acoustic properties including sound speed and attenuation via partial differential equation (PDE) recovery from noisy and incomplete wave field measurements. We encode the knowledge of the assumed PDE, i.e., the wave equation, into the loss function to be minimized during training, and formulate the coefficients of the wave equation within the spatially two-dimensional (2D) region of interest as matrices of low ranks. The method is validated using datasets of 2D wave propagation.