Dual-Input Autoencoder-Based Method for Enhancing Repeatability in Time-Lapse Seismic Data

Time-lapse seismic exploration is a crucial tool for monitoring subsurface changes, with applications in hydrocarbon production assessment and CO2 migration tracking in carbon capture and storage (CCS) projects. However, accurate detection of subsurface changes is challenging due to non-repeatable effects. Traditional cross-equalization techniques and advancements in machine learning have been aimed at addressing these challenges, but often face limitations, particularly in handling real-world variables. We present a dual-input autoencoder (DIAE) designed to improve the repeatability of time-lapse post-stack seismic data by simultaneously processing baseline and monitoring datasets. The DIAE model introduces a custom loss function that combines reconstruction loss and repeatability loss, enabling flexible control between signal preservation and noise reduction. We evaluate the proposed method using two real-world datasets: the Enfield oil field dataset from Western Australia and the Sleipner CCS dataset from Norway. The results demonstrate that DIAE effectively suppresses non-repeatable noise, while minimizing signal distortion. By achieving a practical balance between repeatability enhancement and signal preservation across different geological settings, DIAE presents a robust solution for improving time-lapse seismic monitoring in both production and CCS environments.