RNA method for nonlinear inversion

Considering the powerful ability of neural networks in representing high-dimensional functions, here we propose a reinforcement learning (RL) framework (namely RNA), which dynamically learns and constructs the global distribution of the objective function with a neural network, to realize moment tensor inversion. The learned objective function is then utilized to guide the adaptive resampling during each iteration, achieving both global optimization and enhanced inversion efficiency.