ABSTRACT Multiples are generally considered coherent noise in conventional seismic migration. If they are not appropriately separated or eliminated from the observed reflection data, it can result in significant artefacts of the migration image, which will adversely affect subsequent structural interpretation and reservoir description. Inspired by the state‐of‐the‐art model‐driven deep learning methods, we have proposed a self‐supervised deep‐learning approach for multiple elimination. The proposed method contains two parts. The first one is that we use the conventional multiple prediction approach to predict the initial surface‐related multiple reflections. The second part is to build a multiple‐elimination model based on a deep neural network. In the deep neural network, the input is set as the predicted initial surface‐related multiples from a conventional method and the label is set as the observed reflection data with primaries and multiples. Therefore, the proposed approach is a kind of self‐supervised deep‐learning multiple‐elimination model. The deep neural network component of our proposed approach can be interpreted as a corrector in conventional methods that performs amplitude and phase correction on predicted multiples. Moreover, we combine the advantages of L 1 and L 2 loss functions and introduce the attention mechanism to improve the inversion efficiency and accuracy of self‐supervised deep networks. Through some experiments with synthesized and field data, we demonstrate that the proposed self‐supervised deep‐learning approach can effectively and efficiently eliminate multiples from the observed data. It excels in both accuracy and efficiency compared to traditional method.
shi et al. (Thu,) studied this question.