Deep learning-based high-resolution Radon transform and its application to multiple elimination

Abstract Multiple elimination based on Radon transform has been widely applied in industrial production. Conventional Radon transform methods tend to cause primary and multiple energy overlap in the Radon domain, while existing high‑resolution Radon transform methods suffer from low computational efficiency. In recent years, deep learning methods have been introduced into the field of multiple elimination. However, the highly nonlinear nature of data in the time‑space domain increases the difficulty of model training. To address these issues, this paper proposes a deep‑learning‑based high‑resolution Radon transform method. By employing a U‑net architecture, the method establishes a nonlinear mapping from the adjoint solution to the high‑resolution solution in the Radon domain, replacing the iterative inversion process in conventional high‑resolution Radon transforms. This enables the rapid acquisition of high‑resolution Radon‑domain data for multiple elimination. Compared with time‑space domain data, Radon‑domain data exhibit lower complexity and higher sparsity, which enhances both the convergence speed and stability of network model training. Furthermore, adjusting the curvature parameter in the Radon transform allows for data compression during training, reducing the size of the data to be processed and further improving training efficiency. Examples using both synthetic data and field data verify the effectiveness of the proposed method and demonstrate its advantages over conventional time‑space domain algorithms.