Unsupervised deep learning seismic data random noise attenuation with early stopping

Abstract

Suppression of seismic random noise is one critical step in seismic data processing. In recent years, the outstanding ability of deep learning to denoise seismic data is impressive. The unsupervised deep image prior (DIP) model has achieved promising denoising results without training labels. However, during training, these models first learn the effective seismic events in the noisy data, and then pick up the random noise afterwards, i.e. overfitting. Thus, the practicability of DIP hinges on good early stopping (ES) that catches the potentially noise-free seismic data. In this respect, most DIP studies only demonstrate potential of the models by showing the peak performance accessing the ground truth as reference, but provide no clue about how to operationally catch near-peak output without the ground truth. In this paper, we investigate the ES strategy in seismic data denoising using DIP method, which consistently detects the performance of reconstruction sequence by observing its running variance (VAR). The adopted ES method incurs low computational overhead. Numerical tests on 2D/3D synthetic and field data demonstrate that compared with other stopping criteria, the ES method exhibits superiority in suppressing random noise and preserves the effective signals better.