Noise types and their attenuation in towed marine seismic: A tutorial

Abstract

The presence of noise in towed marine seismic data is a long-standing problem. The various types of noise present in marine seismic records are never truly random. Instead, seismic noise is more complex and often challenging to attenuate in seismic data processing. Therefore, we examine a wide range of real data examples contaminated by different types of noise including swell noise, seismic interference noise, strumming noise, passing vessel noise, vertical particle velocity noise, streamer hit and fishing gear noise, snapping shrimp noise, spike-like noise, cross-feed noise and streamer mounted devices noise. The noise examples investigated focus only on data acquired with analogue group-forming. Each noise type is classified based on its origin, coherency and frequency content. We then demonstrate how the noise component can be effectively attenuated through industry standard seismic processing techniques. In this tutorial, we avoid presenting the finest details of either the physics of the different types of noise themselves or the noise attenuation algorithms applied. Rather, we focus on presenting the noise problems themselves and show how well the community is able to address such noise. Our aim is that based on the provided insights, the geophysical community will be able to gain an appreciation of some of the most common types of noise encountered in marine towed seismic, in the hope to inspire more researchers to focus their attention on noise problems with greater potential industry impact.

De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes

“…Another potential challenge is the presence of noise that will increase the uncertainty of the proposed method. Seismic noise is never truly random but exhibits specific patterns (Hlebnikov et al ., 2021). Therefore, in the presence of a low signal‐to‐noise ratio, the results can be biased by such noise patterns and compromise the quality of the interpolated primary events.…”

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

“…In step 1, we randomly change the frequency content of the noise data in masks from mono‐frequencies to a wide range of frequencies. If we expect to have noise that partly covers the traces (e.g., the swell, spike‐like and velocity sensor noise in marine data; Hlebnikov et al., 2021) we can randomly change the temporal location of masks to teach the network to look for horizontal incoherent noise in smaller parts of a trace instead of the entire trace.…”

“…One type of noise we address in this study is coherent along the recorded traces (vertical direction) and incoherent in the offset (horizontal) direction. This trace‐wise noise includes cross‐feed noise (after stacking), swell noise, streamers bird noise, velocity sensor noise and spike‐like noise in marine data (Hlebnikov et al., 2021), receivers malfunctioning noise and noises resulting from external sources of energy in common receiver gathers (Strobbia et al., 2022). Also, in pseudo‐deblending, the blending noises become trace‐wise incoherent after specific rearrangements of data (S. Wang et al., 2021).…”

Machine-learning Based Noise Attenuation of Field Seismic Data using Noise Data Acquisition

“…위 과정들 중 잡음 억제는 탄성파 자료의 품질에 큰 영향 을 주는 요인 중 하나이므로 주의 깊게 설계되고 수행되어 야 한다. 탄성파 자료에 기록되는 잡음은 선박 소음, 해류의 흐름이나 파도, 장비 운용 등으로 인해 발생한 무작위 잡음 과 다중 반사파(multiple), 공기파(air wave), 표면파(surface wave) 등과 같은 일관성 잡음이 있다 (Ebadi, 2017;Hlebnikov et al, 2021). 탄성파 탐사를 수행하는 과정에서 잡음이 발 생하는 요인을 완전히 억제하는 것은 매우 어렵고 탐사환 경에 따라 상이하기 때문에 탄성파 자료마다 서로 다른 잡 음특성을 가지고 있다 (Kragh and Christie 2002;Nasser et al, 2016;Waage et al, 2019) (Li et al, 2018;Zhao et al, 2019;Liu et al, 2018;Jun et al, 2020)…”