Full waveform inversion based on Adam algorithm with optimized parameters

For the conventional Full Waveform Inversion (FWI), the gradient is not optimized, and extra wavefield extrapolations are needed to calculate the step-length for velocity updating usually, which slows down the convergence rate and the inversion accuracy. This paper introduces Adam, a deep learning algorithm into FWI, so as to improve thse convergence efficiency and inversion accuracy with less computational cost by optimizing the gradients and providing the optimized model update value directly. Meanwhile, we systematically analyze the effect of different parameters on the FWI by lots of numerical experiments and give optimized parameters which are more suitable for FWI. Model tests demonstrate that, the FWI based on Adam algorithm with the optimized parameters has much higher convergence rate and inversion accuracy than both the FWI based on Adam algorithm with the default parameters and the FWI based on limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm.