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.
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Madusanka Madiligama
University of Mississippi
Piya Amara Palamure
Likun Zhang
Shanghai Jiao Tong University
University of Mississippi
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Madiligama et al. (Mon,) studied this question.
synapsesocial.com/papers/68d7cc66eebfec0fc5238968 — DOI: https://doi.org/10.1121/10.0039343
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