PD-VBS: Real Seismic Image Denoising With Pixel-Shuffle Down-Sampling and Visible Blind-Spots

Suppressing random noise is an effective way to improve the signal-to-noise ratio (SNR) of seismic data. Supervised deep-learning methods have recently been widely applied to seismic image denoising. However, these methods require a large amount of noise-free data to train the network, which is unavailable in practical applications. Moreover, most of these denoising methods focus on removing random noise, assuming that the noise is zero-mean and independent of seismic signals. In field applications, real seismic noise often exhibits band-limited and spatial correlations. We propose an unsupervised learning method to train a denoising network using only noisy images, termed Pixel-shuffle Down-sampling and Visible Blind-Spots (PD-VBS). First, we propose to utilize Pixel-shuffle Down-sampling (PD) to destroy the spatial correlation of real seismic noise, followed by feeding the data into the blind-spots network (BSN). Next, we introduce additional input derived from the input data with a finer stride PD to compensate for the information loss induced by both the BSN and PD mechanisms. Experimental results on both synthetic and field data show that our proposed Pixel-shuffle Down-sampling and Visible Blind-Spots (PD-VBS) can effectively remove noise while preserving valid signals, compared with traditional denoising methods and state-of-the-art deep-learning models.