Abstract
Seismic field data are usually contaminated by random or complex noise, which seriously affect the quality of seismic data contaminating seismic imaging and seismic interpretation. Improving the signal-to-noise ratio (SNR) of seismic data has always been a key step in seismic data processing. Deep learning approaches have been successfully applied to suppress seismic random noise. The training examples are essential in deep learning methods, especially for the geophysical problems, where the complete training data are not easy to be acquired due to high cost of acquisition. In this work, we propose a natural images pre-trained deep learning method to suppress seismic random noise through insight of the transfer learning. Our network contains pre-trained and post-trained networks: the former is trained by natural images to obtain the preliminary denoising results, while the latter is trained by a small amount of seismic images to fine-tune the denoising effects by semi-supervised learning to enhance the continuity of geological structures. The results of four types of synthetic seismic data and six field data demonstrate that our network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.
Highlights
Seismic signals recorded by sensors onshore or offshore are usually contaminated by random noise, which leads to poor seismic data quality with low signal-to-noise ratio (SNR)
We use transfer learning to achieve seismic image denoising through pre-training on natural images
The PSNR, MSE, and SSIM of the pre-trained network have been greatly improved in the first stage, but some details of the seismic events are not processed well enough
Summary
Seismic signals recorded by sensors onshore or offshore are usually contaminated by random noise, which leads to poor seismic data quality with low signal-to-noise ratio (SNR). There have been various denoising methods in recent decades such as prediction-based noise suppression method: t-x predictive filtering [1,2] and non-stationary predictive filtering [3,4], the sparse transform domain method including wavelet transform [5], curvelet transform [6], seislet transform [7], contourlets transform [8], dictionary learning-based sparse transform [9], singular spectrum analysis [10,11], etc These traditional methods separate noise from signals mainly based on the features of signal and noise itself or their distribution characteristics in different transform domains. The features of seismic signals are complex in real situations and the distribution of characteristics of the signal and noise are overlapped in transform domain, so it is almost impossible to accurately separate the noise from noisy signals
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