Multi-Scale GAN with NSCT Prediction for Seismic Data Denoising

In this paper, we focus on denoising seismic signals effectively to obtain high-quality data, which is crucially important for oil-gas reservoir prediction and seismic interpretation tasks. The rapid progress of deep learning has brought new development opportunities to seismic oil and gas exploration technologies. However, current deep learning-based seismic denoising models have limited learning ability due to insufficient extraction features in strong noise backgrounds. For this reason, we develop a new multi-scale generative adversarial networks (GAN) with transform prediction for seismic signal denoising. First, we develop a deep multi-scale diversion fusion (MSDF) network in a GAN generator considering the advantages of combining GAN and convolutional neural networks. MSDF network primarily contains several MSDF blocks that mine abundant long-short path features in multiple receptive fields to restore seismic signals in a rough to detailed manner. Additionally, current deep learning-based seismic denoising models only exploit features in the spatial domain, not considering high-frequency characteristics, which results in insufficient high-frequency details when reconstructing seismic data. So, we propose to predict high-frequency components during learning by employing the superior non-subsampled contourlet transform (NSCT), which further preserves the better global topological structure and local texture characteristics of MSDF in GAN generator than the spatial domain, promoting the discrimination ability in GAN discriminator. The qualitative and quantitative results on our constructed synthetic dataset and actual seismic data demonstrate that the proposed method surpasses other deep learning-based approaches in realizing higher signal-to-noise ratio, as well as mining more effective high-frequency signals.