Reducing bias due to noise and attenuation in open-ocean echo integration data

TLDR

The data were collected on vessels of various designs, none of which were built for high‑quality acoustic measurements, and a full range of weather extremes could introduce large biases in Sv. The study aimed to remove first‑order biases and improve processing efficiency by applying a sequence of new and existing data‑processing filters in a semi‑automated procedure. The authors measured mean volume backscattering strength (Sv) at ocean‑basin scale using 38‑kHz hull‑mounted echosounders on ships of opportunity, then applied filters designed to mitigate impulsive, transient, and background noise, as well as a filter to detect signals attenuated by air bubbles beneath the transducer. The filters were applied to transits across the Southwest Pacific, Indian, and Southern Oceans, producing quality‑controlled Sv datasets now publicly available, and they may be useful for other open‑ocean acoustic observing endeavors to reduce bias.

Abstract

Abstract Measurements of mean volume backscattering strength (Sv, dB re 1 m−1) at ocean-basin scale were made using 38-kHz hull-mounted echosounders on ships of opportunity as part of Australia's Integrated Marine Observing System. The data were collected on vessels of various designs, none of which were purposely built for collecting high-quality acoustic data. A full range of weather extremes affected the quality of the data and could cause large biases in Sv. To remove first-order biases and improve processing efficiency, a sequence of new and existing data processing filters were applied in a semi-automated procedure. These filters were designed to mitigate the effects of three types of noise: impulsive (less than one ping), transient (multiple pings), and background (hours or longer). A filter was also applied to identify signals that were attenuated by air bubbles beneath the transducer. These filters were applied to data from transits across the Southwest Pacific, Indian, and Southern Oceans to produce quality-controlled Sv datasets that are now available from a publicly accessible repository. These filters may be relevant to other open-ocean acoustic observing endeavours, and one or more could be used to mitigate bias in data from a range of acoustic applications.