Statistical characteristics of under-ice noise on the Arctic Chukchi Plateau

Abstract

In the context of global warming leading to rapidly changing Arctic sea ice and the environment, it is necessary to understand the statistical characteristics of noise under existing Arctic ocean environmental conditions. The data recorded from August 1, 2018 to November 2, 2019, on the Arctic Chukchi Plateau, have been studied to analyze the relationship between the ice transient events and the non-Gaussian statistics of under-ice noise. The ice-generated transient noise largely contributes to the under-ice noise environment, and the total under-ice noise exhibits non-Gaussian statistics due to the occurrence of these ice transients. The number of ice transients has a strong negative correlation with the characteristic index α, meaning that the higher the occurrence of transient events, the stronger the non-Gaussian statistics of under-ice noise. Stronger non-Gaussian noise with full ice coverage is observed compared to partial ice coverage. The under-ice noise in 0.5–1 kHz exhibits the strongest non-Gaussian statistics, followed by 1–4 kHz, while it is weakest in 20–500 Hz. The statistics of ice transients and under-ice noise have been provided in this paper, which can be used in sonar detection algorithms and have important significance for the performance prediction and optimization of sonar processors.

Depth Spatial Characterization of Marine Environmental Noise in the Zengmu Basin

“…Its sources are diverse, including the movement of seawater, the impact of wind and atmosphere on sea surface noise, changes in water layers due to movement or melting, alterations in submarine geological structures, sounds produced by marine organisms, and noise generated by man-made sources [1][2][3][4][5]. There are differences in marine environmental noise under different time and space conditions [6][7][8][9][10], which makes it extremely difficult to predict marine environmental noise [11,12]. Based on a large amount of data from environmental noise surveys, previous studies have derived empirical prediction formulas and conclusions that are applicable within a specific range [13,14].…”