The integration of conventional high-performance full-waveform inversion (FWI) algorithms with deep learning frameworks is an innovative and promising research direction, with the potential to significantly enhance and broaden the application prospects of this field. Automatic differentiation with backpropagation techniques can derive gradients of model parameters in an equivalent manner to that of adjoint methods; however, it requires a substantial amount of computer memory, particularly when considering time-step layers. Additionally, some excellent objective functions suitable for FWI are not available in deep learning frameworks. In comparison, the adjoint method based on effective boundary storage technology, offers greater practicality for calculating gradients of various objective functions. Therefore, this paper proposes a novel approach toward FWI that integrates deep learning optimization with high-performance gradient computation. In particular, the proposed method inputs model parameter gradients from a custom objective function into the deep learning framework. Herein, we use the acoustic equation with variable density as an example to demonstrate how a convolutional objective function, along with its corresponding velocity and density gradients, can be utilized for optimized inversion, multiscale inversion, and deep network parameterization-based multiscale inversion within the deep learning framework. This approach provides a paradigm for deep learning-optimized FWI, which we apply to both synthetic and field data scenarios.
