Incorporating Structural Constraint Into the Machine Learning High-Resolution Seismic Reconstruction

Seismic high-resolution (HR) reconstruction is a crucial process for identifying increasingly thin layers from observed seismic data. Nowadays, machine learning (ML) has been adopted in seismic resolution improvement; however, most of the ML-based methods directly use 1-D neural networks and ignore the spatial information along seismic traces. Thus, these methods cause poor stability and accuracy issues in improving the resolution of multidimensional seismic data. In this article, we propose to incorporate a structural constraint into a neural network framework to perform seismic HR reconstruction. The loss function of our network consists of two parts. One part is used to extract useful HR seismic trace features from the training set generated by the well-log data so that the network can facilitate the solution from low resolution (LR) to HR. More importantly, the other part is used to preserve the reflection structure features of seismic data, which guarantees the stability and accuracy of HR reconstruction results. Tests on synthetic and field examples verify that the proposed method can provide a better reconstruction result in terms of resisting noise, retrieving thin layers, and preserving lateral structure than the traditional method and the ML-based method with the same network framework.