Random Noise Attenuation of Sparker Seismic Oceanography Data with Machine Learning

Abstract

Abstract. Seismic oceanography (SO) acquires water column reflections by seismic exploration compensating for the drawbacks of conventional physical oceanographic equipment. Most SO studies obtain data using air guns, which have relatively low-frequency bands. For higher-frequency bands at a low exploration cost, using a smaller seismic exploration system, such as a sparker source with a shorter receiver length, would be an alternative. However, the sparker source has a relatively low energy and consequently produces data with a low signal-to-noise (S / N) ratio. To solve the problem of the low S / N ratio of sparker SO data, we applied machine learning. The purpose of this study is to attenuate the random noise in the East Sea sparker SO data without distorting the true shape and amplitude of water column reflections. A denoising convolutional neural network (DnCNN) that successfully suppresses random noise in a natural image is adopted as the machine learning network architecture. One of the most important factors of machine learning is the generation of an appropriate training dataset. We have generated two different training datasets using synthetic and field data. Models trained with the different training datasets are applied to the test data, and the denoised results are quantitatively compared. The trained models are applied to the target seismic data, i.e., the East Sea sparker water column seismic reflection data, and the denoised seismic sections are evaluated. The results show that machine learning can successfully attenuate the random noise of sparker water column seismic reflection data.