A survey of marine seismic acoustics: Water column imaging, environmental impacts, and de-bubbling
Abstract
Marine seismic surveys are crucial in geophysics, oil exploration, and water column acoustic reflection imaging. These surveys, particularly multichannel ones, help observe mesoscale features like ocean fronts, eddies, and internal waves. Airguns, commonly used as sound sources in these surveys, generate a desired primary pulse followed by unwanted bubble oscillations that are termed bubble waves (BWs). These BWs can degrade seismic data accuracy, especially in shallow water and when capturing faint water column reflections. This article reviews the history of seismic sound sources, bubble formation physics, source signatures, and the acoustic multipath structures of the signal. It also covers applications of seismic data in water column imaging and sound propagation for environmental impact assessments. Given that bubble oscillations affect data quality, the article highlights recent advancements in mitigating BW effects, including larger airgun arrays, debubbling through simulation techniques, and advanced signal processing. Furthermore, it explores the potential of artificial intelligence (AI) techniques, such as physics-informed neural networks and hybrid methods, to enhance seismic data quality. These AI-based approaches aim to improve imaging accuracy and reliability, particularly for faint water column reflections.
Read the paper
Related studies
- Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
- Physics of Partially Saturated Porous Media: Residual Saturation and Seismic-Wave Propagation
- Modern Imaging Using Seismic Reflection Data
- Effects of marine seismic surveys on free-ranging fauna: a systematic literature review
- A review of the recent seismic activity in the southern part of egypt (upper egypt)
- A review of research on mechanism of seismic attenuation and energy compensation
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.