# Accelerating High-Resolution Seismic Imaging by Using Deep Learning
## 1 Introduction  
Understanding the underground structure is essential to energy exploration, avoiding natural disasters, and studying the evolution of the Earth. Pre-stack time migration (PSTM) has been widely used in academia and industry, mainly because of its robustness against velocity errors. In fact, neither acoustic or elastic approximation can accurately describe the seismic wave propagation in the real Earth, for example, strong attenuation can exist in the weathering layers. However, the conventional PSTM ignores the viscosity of the medium, which leads to the absence of high-frequency components and results in low-resolution migration images. [PERSON] [1; 2] introduced an effective Q approach (QPSTM) to compensate for the attenuated energies, which are crucial for high-resolution imaging. However, in contrast to the conventional PSTM, the QPSTM approach involves performing an additional integral over all the frequencies, which is time consuming and prevents the application of this approach to large-scale problems. To improve the computational efficiency, [PERSON] [3] distributed the heavy workload to graphics processing units (GPUs) and optimized the calculation at the programming level. Although the use of the GPUs is beneficial, the approach remains less efficient for solving realistic problems; for instance, performing a realistic 3D QPSTM can take several months even when using advancedGPU clusters. Consequently, we continue to seek better solutions, and the emerging machine learning algorithms appear to be a promising tool to solve this problem.  
The use of artificial intelligence (AI), involving deep learning (DL) [4; 5] approaches, has achieved success in many research areas, such as natural language processing [6; 7; 8], image classification [9; 10; 11; 12; 13; 14], image super-resolution [15; 16; 17; 18], image retrieval [19; 20; 21; 22], and so on. In addition, geophysicists have attempted to adopt DL to solve particular problems: [PERSON] [23] employed the support vector regression method to solve the missing-data interpolation problem. To perform seismic data interpolation, [PERSON] [24] designed an eight-layer residual learning network based on a convolutional neural network (CNN) [25]. [PERSON] [26] adopted the widely used classification networks to detect faults from migration images. [PERSON] [27] introduced the deep neural network to extract features from 2D prestack gathers. [PERSON] [28] adapted the DL method to velocity estimation without requiring any prior information. [PERSON] [29] introduced the DL strategy into seismic data interpolation to provide accurately reconstructed dense data. [PERSON] [30] proposed the identification of the first arrivals by using convolution networks. [PERSON] [31] introduced a regularized elastic full-waveform inversion by using DL. In addition, [PERSON] [32] used a progressive deep transfer learning approach to predict the missing low-frequency components in seismic data.  
Considering this background, in this study, an end-to-end high resolution seismic imaging method was developed by exploiting DL to realize an affordable QPSTM. The proposed model is an encoder-decoder framework that performs a detailed feature extraction of the seismic images.