Dropout-Based Robust Self-Supervised Deep Learning for Seismic Data Denoising

Incoherent noise suppression is an indispensable step in seismic data processing. Recently, deep learning (DL) methods have gained commendable success in seismic data denoising, one of which is the supervised DL denoising method using clean data as the training label, whereas the cost of obtaining clean data is high. We investigate a robust self-supervised DL denoising method without using clean data. Bernoulli-sampled training pairs of the raw noisy data produced by the dropout layer are served to train the NN, and a Monte Carlo (MC) self-integrated technique results in further improving the denoising quality of the trained NN during the testing. Compared with the f-x deconvolution (FXDECON), deep image prior (DIP), and sparse autoencoder (SAE) methods via synthetic and real data examples, the proposed method outperforms these methods for enhancing the signal-to-noise ratio (SNR) and reducing the signal loss.