## 1 Introduction
As a result of these limitations, deep learning-based ground roll suppression methods are gradually shifting from supervised methods that require annotated data to unsupervised methods that do not require labeled data. [PERSON] [48] proposed a combined unsupervised and supervised deep learning method to implement seismic ground roll attenuation. [PERSON] [49] studied an unsupervised ground roll suppression method based on NMO (Normal Moveout), which achieved ground roll suppression without using labeled data for the first time. This method first flattens the reflected waves through NMO correction, enhances the self-similarity of the reflected waves, and reduces the self-similarity of the ground roll. Then, by utilizing the multi-scale self-similarity feature extraction capability of the deep learning network, it prioritizes the extraction of strongly self-similar reflected waves. By selecting a specific number of iterations, the ground roll and reflected waves are separated, thus achieving ground roll suppression. However, this method can easily introduce phase shifts into the seismic records after ground roll suppression. This paper proposes the WPI-SD (wavelet prior information for deep learning in seismic data) network, an unsupervised learning network that leverages prior information in deep learning and the distribution characteristics of seismic data for suppressing ground roll noise in deep pre-stack seismic records. The proposed method performs wavelet decomposition on the seismic records containing ground roll to obtain different wavelet components in different frequency bands with varying ground roll energy proportions. A loss function is then designed, which includes a sparse representation regularization term that controls the weight distribution of each sparse representation by modifying the coefficient difference between the input seismic records and the network output for each wavelet component. The loss function assigns smaller weights to wavelet components with strong ground roll and larger weights to wavelet components almost free of ground roll, leading to a higher weight for real reflected waves. By minimizing the loss function, the WPI-SD network model parameters are adjusted to extract reflected waves from the original seismic records, achieving ground roll suppression. Compared to existing deep learning-based ground roll suppression methods, the WPI-SD network has two distinct features. Firstly, the method employs weighted processing of wavelet components based on the energy distribution characteristics of ground roll and effective signals in different frequency bands. This involves increasing the coefficients of wavelet components with almost no ground roll while decreasing the coefficients of those with ground roll. To achieve this, a ground roll suppression loss function is designed to prioritize the extraction of effective reflection signals during the deep learning network's learning process. Secondly, the WPI-SD network does not require a large amount of paired training data. It leverages the ability of deep learning networks to automatically learn feature representations, optimize reconstruction loss functions, and learn data distribution characteristics. With a certain number of iterations, it can learn the features of different waveform signals from input data and separate ground roll from real reflected waves, without requiring a large amount of paired training data.