Intelligent Suppression of Marine Seismic Multiples Using Deep Learning Methods

Multiple suppression is an important element of marine seismic data processing. Intelligent suppression of multiples using artificial intelligence reduces labor costs, minimizes dependence on unknown prior information, and improves data processing efficiency. In this study, we propose an intelligent method for suppressing marine seismic multiples using deep learning approaches. The proposed method enables the intelligent suppression of free-surface-related multiples from seismic records. Initially, we construct a multi-category marine seismic multiple dataset through finite difference forward modeling under different boundary conditions. We use various models and data augmentation methods, including sample rotation, noise addition, and random channel omission. Then, we apply depthwise separable convolution to develop our deep learning Mobilenet-Unet model. The Mobilenet-Unet framework significantly reduces the number of operations required for multiple elimination without sacrificing model performance, ultimately realizing the optimal multiple suppression model. The trained Mobilenet-Unet is applied to the test set for verification. Moreover, to determine its generalization ability, it is implemented to seismic records containing multiples generated by two marine geophysical models that were not included in the training process. The performance of Mobilenet-Unet is also compared with that of different network structures. The results indicate that, despite its small size, our proposed Mobilenet-Unet deep learning model can rapidly and effectively separate multiples in marine seismic data, possessing reasonable generalization ability.