EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Long-term trend analysis of deep-ocean acoustic noise data

Sei-Him Cheong, Stephen P Robinson, Peter M Harris, Lian S Wang, and Valerie Livina
Sei-Him Cheong et al.
  • National Physical Laboratory,

Underwater noise is recognised as a form of marine pollutant and there is evidence that over exposure to excessive levels of noise can have effects on the wellbeing of the marine ecosystem. Consequently, the variation in the ambient sound levels in the deep ocean has been the subject of a number of recent studies, with particular interest in the identification of long-term trends. We describe a statistical method for performing long-term trend analysis and uncertainty evaluation of the estimated trends from deep-ocean noise data. This study has been extended to include  measured data  from four monitoring stations located in the Indian (Cape Leeuwin & Diego Garcia), Pacific (Wake Island) and Southern Atlantic (Ascension Islands) Oceans over periods spanning between 8 to 15 years. The data were obtained from the hydro-acoustic monitoring stations of the Preparatory Commission for the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO). The monitoring stations provide information at a sampling frequency of 250 Hz, leading to very large datasets, and at acoustic frequencies up to 105 Hz.

The analysis method uses a flexible discrete model that incorporates terms that capture seasonal variations in the data together with a moving-average statistical model to describe the serial correlation of residual deviations. The trend analysis is applied to time series representing daily aggregated statistical levels for four frequency bands to obtain estimates for the change in sound pressure level (SPL) over the examined period with associated coverage intervals. The analysis demonstrates that there are statistically significant changes in the levels of deep-ocean noise over periods exceeding a decade. The main features of the approach include (a) using a functional model  with terms  that represent both long-term and seasonal behaviour of deep-ocean noise, (b) using a statistical model to capture the serial correlation of the residual deviations that are not explained by the functional model, (c) using daily aggregation intervals derived from 1-minute  sound pressure level averages, and (d) applying a non-parametric approach to validate the uncertainties of the trend estimates that avoids the need to make an assumption about the distribution of the residual deviations.

The obtained results show the long term trends vary differently at the four stations. It was observed that low frequency noise generally dominated the significant trends in these oceans. The relative differences between the various statistical levels are remarkably similar for all the frequency bands. Given the complexity of the acoustic environment, it is difficult to identify the main causes of these trends. Some possible explanations for the observed trends are discussed. It was however observed some stations are subjected to strong seasonal variation with a high degree of correlation with climatic factors such as sea surface temperature, Antarctic ice coverage and wind speed. The same seasonal effects is less pronounced in station located closer to the equator.

How to cite: Cheong, S.-H., Robinson, S. P., Harris, P. M., Wang, L. S., and Livina, V.: Long-term trend analysis of deep-ocean acoustic noise data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21819,, 2020


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