Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data

Abstract

Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal.

Keywords

• OBN
• shear wave noise attenuation
• self-supervised
• deep learning
• adaptive subtraction