Seismic Data Reconstruction via Least-Squares Generative Adversarial Networks With Inverse Interpolation

Seismic data reconstruction is a crucial step in seismic data processing, which faces numerous challenges in accurately capturing the complex patterns and structures within the data. Deep learning is revolutionizing the processing of seismic exploration data, enabling more precise imaging of subsurface structures and improving the detection of potential oil and gas reservoirs. We propose a modified least-squares generative adversarial network incorporating the inverse interpolation (LSGAN-II) method for seismic data reconstruction. Our approach integrates inverse interpolation algorithms into a generative adversarial network (GAN) framework to enhance both the accuracy and efficiency of seismic data reconstruction. Initially, a GAN is employed to predict local event slopes in seismic data, where a least-squares loss function is utilized to improve the learning capacity and convergence speed of the deep learning network. Subsequently, the predicted local event slopes are used as regularization operators in inverse interpolation, effectively enhancing the precision of the reconstructed seismic data. A dual-discriminator-based LSGAN-II is developed to implement the seismic data reconstruction process. This network combines the benefits of both GAN and inverse interpolation techniques, utilizing dual discriminator loss functions and a least-squares error loss function to optimize the prediction of local event slopes and the reconstruction of seismic data. The testing of the LSGAN-II method is conducted using two synthetic datasets and one actual dataset, demonstrating its effectiveness and practicality in seismic data reconstruction.