High-resolution seismic data processing plays a crucial role in the depiction and characterization of reservoir structures, especially when exploration targets become increasingly complex. In recent years, with the rapid development of deep learning technology, it has been increasingly introduced  into high-resolution seismic data processing. Based on a large amount of labeled data, complex nonlinear relationships between low-resolution seismic data and high-resolution seismic data are established. However, the accuracy and stability of the results generated by deep learning in high-resolution data processing highly depend on the accuracy and diversity of training sets. One of the main challenges of practical application of deep learning-based high-resolution reconstruction in production is the sparse well data, which often leads to limited training sets. To address this issue, this paper proposes a deep learning-based high-resolution processing method that integrates the layered structure represented by well data and the spatial geological structure represented by seismic data in the working area by using numerous and realistic training sets. The establishment of the training sets includes three steps. (1) Calculate the impedance sequence using well data, fit the amplitude distribution of the high-frequency part of the impedance using a Gaussian matching function to obtain a probability density function (PDF), and generate a series of impedance sequences that conform to the statistical distribution of the well data. (2) On the basis of the impedance sequences, establish a two-dimensional horizontal impedance model, and gradually add folding deformation, dip deformation, and fault deformation to generate a two-dimensional impedance model containing various geological patterns. (3) Calculate the reflection coefficient using the impedance model, and then convolute the low-frequency and high-frequency wavelets with the reflection coefficient model to obtain the training sets. By automatically generating a large number of training sets with underground geological knowledge, the trained network can estimate stable and accurate high-resolution results. The framework of deep learning is composed of two parts: an encoding part that extracts features from the input data and a decoding part that reconstructs the output from the extracted features. In addition, residual modules are incorporated into the framework to enhance performance by enabling the network to learn more effectively from the training sets, resulting in a better balance between computational accuracy and efficiency. Synthetic data and field data tests show that the proposed method has better robustness to noise and can yield more accurate and laterally more consistent high-resolution results compared to traditional deep learning methods.