The interest in WCI measurements is reflected by ongoing implementation of WCI functionality into modern MBES and development of respective powerful online and post processing software packages for hydrographic and fishery applications. Novel and compelling applications of WCI from MBES range from qualitative descriptions of fish school behavior and morphological characteristics, oil and gas leakage detection, kelp ecosystem mapping and aquaculture monitoring, to assessing the mean abundance of marine macro-litter and providing other valuable insights to marine systems. Indeed, the wide area insonified with a single swath (typically covering about 120°) differentiates MBESs from SBES whose main beam widens less than 20° and therefore requires far more survey time to achieve equivalent coverage. Nowadays MBES WCI provides numerous opportunities for fisheries and eco-biological research, especially when combined with the ability to acoustically cover a larger volume of the water column. Single and split-beam echosounder (SBES) technology existed since at least the 1940s and has been traditionally used for the purpose of the evaluation of fish populations. Over the last decade or so, technological advancements in MBES hardware and software have facilitated the use of MBES data for water-column imaging (WCI), which has emerged as viable means of hydrographic data quality control and found increasing use in oceanographic studies. Multibeam echosounders (MBESs) are active sonars and have been historically designed for hydrographic purposes, such as submerged obstacle detection, bathymetry and seabed characterization. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies.

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