Repeatability enhancement of time-lapse seismic data via a convolutional autoencoder

In an ideal case, the time-lapse differences in 4-D seismic data should only reflect the changes of the subsurface geology. Practically, however, undesirable discrepancies are generated because of various reasons. Therefore, proper time-lapse processing techniques are required to improve the repeatability of time-lapse seismic data and to capture accurate seismic information to analyse target changes. In this study, we propose a machine learning-based time-lapse seismic data processing method improving repeatability. A training data construction method, training strategy and machine learning network architecture based on a convolutional autoencoder are proposed. Uniform manifold approximation and projection are applied to the training and target data to analyse the features corresponding to each data point. When the feature distribution of the training data is different from the target data, we implement data augmentation to enhance the diversity of the training data. The method is verified through numerical experiments using both synthetic and field time-lapse seismic data, and the results are analysed with several methods, including a comparison of repeatability metrics. From the results of the numerical experiments, we can conclude that the proposed convolutional autoencoder can enhance the repeatability of the time-lapse seismic data and increase the accuracy of observed variations in seismic signals generated from target changes.