An Unsupervised Learning Method for Suppressing Ground Roll in Deep Pre-Stack Seismic Data Based on Wavelet Prior Information for Deep Learning in Seismic Data

Abstract

Ground roll noise suppression is a crucial step in processing deep pre-stack seismic data. Recently, supervised deep learning methods have gained popularity in this field due to their ability to adaptively learn and extract powerful features. However, these methods rely on a large amount of clean seismic records without ground roll noise as reference labels. Unfortunately, generating high-quality and realistic clean seismic records for training remains a challenge. To tackle this problem, an unsupervised learning method called WPI-SD (wavelet prior information for deep learning in seismic data) is proposed for ground roll noise suppression in deep pre-stack seismic data. This approach takes into account the distinct temporal, lateral, and frequency characteristics that differentiate ground roll noise from real reflected waves in deep pre-stack seismic records. By designing a ground roll suppression loss function, the deep learning network can learn the specific distribution characteristics of real reflected waves within seismic records containing ground roll noise, even without labeled data. This enables the extraction of effective reflection signals and subsequent suppression of ground roll noise. Applied to actual seismic data processing, this method effectively mitigates ground roll noise while preserving valuable reflection signals, proving its practical significance.