## 1 Introduction  
Seismic high-resolution reconstruction is a key step in processing seismic data to clearly characterize the subsurface geological structure. Owing to the band-limited characteristics inherent in raw seismic data, predicting high-resolution data is always ill-posed, resulting in several solutions applicable to the observation data. Therefore, many regularization techniques with difference operators impose the estimated high-resolution results complying with the certain distribution in terms of prior information for mitigating ill-posedness. In our opinion, regularization can mainly be divided into two categories: smooth regularization and sparse regularization. Typically, the first category considers prior knowledge by minimizing the \(\ell_{2}\) norm of the low-order spatial derivative of the model, thus applying the results to the smooth solution [1, 2]. Although this category can provide stable high-resolution data, the discontinuous geologic bodies in the results are obscured. Sparse regularization, such as the \(\ell_{1}\) norm, can describe a blocky stratigraphic interface characterized by abrupt changes under the assumption that the strong reflectivity coefficient is sparse [3, 4]. However, sparse regularization suppresses weak reflection and thin-layer structure and makes thin-layer identification difficult [5]. Therefore, many complex regularizations are designed to consider both continuous and block structures to further improve sparse solutions [6; 7]. These methods often need to design complex solution systems and rely on strong empirical knowledge, resulting in poor adaptability to complex situations. To sum up, all the above regularization methods depend on the mathematical model of the specific structure (smoothness and sparsity), and these methods are referred to as model-driven methods. In recent times, deep learning (DL) has demonstrated notable achievements in the domain of seismic data processing and interpretation [8; 9; 10; 11; 12]. Different from traditional model-driven methods, DL is a type of data-driven approach that can capture parameter characteristics and supplement additional constraints feasibly and adaptively through multiple processing layers with adjustable parameters based on a training set. [PERSON] and [PERSON] incorporate CNN-predicted prior information into the objective function of full waveform inversion, leading to the effective reconstruction of salt bodies [13]. [PERSON] and [PERSON] introduce a self-supervised multistep deblending framework leveraging coherence similarity, achieving accurate deblending in blended seismic acquisition [14]. [PERSON] et al. improve seismic structural interpretation by training CNNs on synthetic seismic images generated from diverse, realistic 3D structural models [15]. [PERSON] et al. introduce GRDNet, a fully convolutional framework featuring dense connections, designed for the efficient attenuation of ground roll noise in land seismic data, significantly improving seismic data quality [16]. However, DL's powerful learning ability lies in its large number of optimization parameters, which leads to the need for sufficient high-quality training sets. [PERSON] et al. and [PERSON] et al. use the synthetic training dataset to pre-train the CNN, and then use the labeled data in the actual survey to fine-tune the pre-training network by introducing the transfer learning strategy, so as to alleviate the problem of insufficient labeled data [17; 18]. [PERSON] et al. and [PERSON] et al. use two sub-CNNs to approximate the forward and inverse processes simultaneously, and finally add unlabeled data (i.e., seismic data) to the training stage [19; 12]. By adding physical constraints, the parameters of DL are further optimized and the accuracy of prediction results is improved under the condition of a limited training set [20; 21]. In this paper, a data-driven framework based on deep learning aimed at improving seismic data resolution is presented.