Similarity-Informed Self-Learning and Its Application on Seismic Image Denoising

Abstract

Seismic image denoising is essential to enhance signal-to-noise ratio (SNR) of seismic images and facilitate seismic processing and geological structure interpretation. With the development of deep learning (DL), several DL based models have been proposed for seismic image denoising. However, the commonly used supervised DL based denoising models require noise-free data as training labels, yet noise-free data is often difficult to be obtained in field application scenarios. By considering the similarity of seismic images, we propose a similarity informed self-learning (SISL) to address seismic image denoising in the absence of noise-free seismic images. To accurately preserve valid seismic signals when constructing training pairs, we develop a specialized workflow, termed the similar image sampler. In this way, we can fully use the self-similarity of noisy seismic images to build training pairs and then train a denoising model. Moreover, to effectively attenuate random noise, we propose a hybrid loss function with a regularization constraint to availably retain valid seismic events. After comparing with traditional denoising methods and several state-of-the-art unsupervised DL models, the experiment results from synthetic and field data quantitatively and qualitatively demonstrate the effectiveness and the stability of the proposed SISL model for seismic image denoising.