Self-Supervised Seismic Resolution Enhancement

The concept of neural network (NN)-based seismic resolution enhancement has gained a lot of traction recently. Yet, the majority of works on the topic rely on training NNs on synthetic data via a supervised learning strategy, often encountering generalization issues on real data. To address this problem, we develop a self-supervised learning (SSL) method for seismic resolution enhancement. Specifically, we reinterpret seismic resolution enhancement as a frequency extension task, particularly focusing on the reconstruction of high-frequency components. Initially, we warm up the NN using the original/available band-limited data as pseudolabels, with input data derived from filtering out high-frequency elements from the data. Subsequently, the network undergoes iterative data refinement (IDR), where pseudolabels are predicted from the NN trained in the previous epoch, and input data are obtained by filtering out high-frequency components from these predictions. Based on this strategy, we also present a hybrid framework for simultaneous seismic denoising and resolution enhancement. During the whole training, we used multiloss constraints to enhance the network performance. The efficacy of our method is demonstrated through tests on both synthetic and field data.