Abstract

Marine seismic reflection surveys use airguns to generate repetitive high energy sound signals to image the structure of the seafloor. To better mitigate against the impact of airgun pulses on marine mammals, safety criteria are defined to ensure marine mammals are not exposed to high levels of acoustic energy. Accurate prediction of the sound received levels away from the airguns is required for conducting effective marine mammal monitoring. In this study, measurements by a horizontal hydrophone array towed by the R/V Marcus G. Langseth behind a seismic source array have been used to characterize short-range propagation of airgun pulses and predict the acoustic energy radiated from a seismic source. Data from the Cascadia Open-Access Seismic Transects seismic reflection survey are used to train a linear regression (LR) and a random forest (RF) model to estimate sound exposure levels (SELs) in short ranges from the airguns. Results show that the LR model does not account for all the variance in data. However, the RF model is able to estimate the SELs with a high coefficient of determination and a low mean squared error. Results from the LR model show that the rate at which SELs decrease in deep water does not match either of the cylindrical or spherical spreading models. Simulations are undertaken to understand this inconsistency as well as the effect of hydrophone group-averaging on data recorded by a seismic hydrophone array.

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