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SFI-Sentre for forskn.drevet innov

CRIMAC - Centre for research-based innovation in marine acoustic abundance estimation and backscatter classification

Alternative title: CRIMAC - Senter for forskningsdrevet innovasjon i marin akustisk mengdemålingsmetodikk og ekkoklassifisering

Awarded: NOK 96.0 mill.

CRIMAC will improve and automate the interpretation of data and images from modern broadband acoustics on research vessels and fishing boats by using cruises and experimental field research, artificial intelligence, drones and inspection technology. The primary objective of the SFI is to advance the frontiers in fisheries acoustic methodology and associated optical methods, and to apply such methods to 1) surveys for marine organisms, 2) fisheries, 3) aquaculture and 4) the energy sector. What are and how do the various parts of marine organisms contribute to broadband backscatter? Complex broadband frequency responses from marine organisms are explored through various numerical models and methods for signal processing. The acoustic properties of mesopelagic fish are important for improving global abundance estimates, and effects of shear viscosity on resonance and target strength of mesopelagic (Khodabandeloo et al., 2021c) and estimation of swimbladder shape (Khodabandeloo et al., 2022) have been published. A software library documenting the signal processing stages of the Simrad EK80 broadband sonar has been developed. The library will be published under an open source license. This is used to control the quality of the Institute of Marine Research's acoustic processing tools, and it is used by CodeLab in their work towards the energy sector. "Leakage" of energy between frequencies has been studied (Khodabandeloo et al., 2021a), and this has been used to update the procedures for the collection of broadband data for scientific missions. What are the broadband frequency responses of marine organisms and other scatterers? Broad band data from mackerel, herring and demersal fish, from probes and ship-based echo sounders, from research vessels and fishing vessels have been collected. These observations are used to build CRIMAC's library for validated targets. Measuring the size of fish is important for the fishing industry, the fish farming industry and for scientific surveys, and we are working with various strategies to estimate length from the broadbanded signal. A net-pen experiment has been carried out on salmon with no access to air for filling their swim bladders, and the broadband signal is monitored over time. Three master theses have been completed based on this experiment (Rong, 2022). Work on the calibration of echo sounders is an important component, and procedures for calibrating broadband echo sounders with different calibration spheres have started. Can machine learning techniques reliably and accurately categorize acoustic backscatter? Modern machine learning algorithms can be used on large volumes on historical acoustic data, and datasets from long time series of acoustic surveys have been established. We have investigated how different preprocessing algorithms affect the performance of the algorithms (Ordoñez et al., 2022). Various algorithms to improve the performance of machine learning algorithms for acoustic species classification have been tested, including semi-supervised algorithms (Choi et al., 2021b). Different echosounder frequencies are used by the algorithms to classify species, and a master's thesis has reviewed which frequencies are important (Holager, 2022). How to utilize acoustic sensors on autonomous platforms, assess uncertainty and utilize the effect of behaviour on acoustic backscatter? The introduction of autonomous or remotely controlled platforms provides an efficient way to deploy acoustic sensors. The platforms can either be driven independently or together with ships. They can be deployed for several objectives, including scout vessels for fishing operations and to extend RV-based acoustic surveys. The kayak drone developed by HI (Totland and Johnsen, 2022) and the Sounder platform developed by Kongsberg will be used in the centre. Different approaches to using these platforms will be explored, including different static and adaptive experimental setups.

Fisheries acoustics is used to monitor the largest fish and krill stocks in the world’s oceans and to study marine ecosystems. A modern fishery without acoustic tools for detection, inspection and monitoring of seabed, schools, and the catching process is unthinkable. New wideband echo sounders offer a new opportunity in this arena for Norwegian science and industry. Science and fishing vessels can not only observe the echo amplitude and density of fish under the vessel, but also utilize the backscattered echo spectrum from the organisms. For simplicity, we prefer to define this as the echo dialect of the objects, as for example, an echo from an individual herring is affected by body shape, swim bladder, body constituent and behavior, and is different from the mackerel “echo dialect". We propose that systematic experimental and in situ research can be used to understand and interpret the different echo dialects from fish and marine organisms. We will further expand on existing multifrequency methods for classification and target sizing by utilizing modern machine learning techniques. This will improve the accuracy of existing monitoring methods and help the fishing skipper to make good catch decisions. Further, direct optical observations from the trawl and use of active selection devises will reduce bycatch. For accurate verification of acoustic recordings, we need continuous optical information from the trawl cod end. This will be achieved with the Scantrol DeepVision system, here tested with active selection devices, and open/closing nets. Discrete samples may then be taken sequentially in deep water, such as in mesopelagic communities. Wideband technology has been miniaturized and can be installed in probes, bottom landers, and surface and underwater unmanned vehicles (drones). We will assess how these can improve scientific monitoring by increased adaptive sampling, and how drones can be used in fishing for forward-mapping and inspection prior to catching.

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SFI-Sentre for forskn.drevet innov

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