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Abstract

The resonance structure of fish echoes is a fundamental aspect of underwater acoustics, enabling the detection and identification of fish species. This review presents an overview of existing research on the subject, highlighting key findings and modeling approaches used to describe the resonance behavior of fish. We also introduce a novel framework for modeling multiscattering effects in fish echoes, providing a comprehensive toolset for researchers and practitioners.

Introduction

Fish echoes have been extensively studied in the context of acoustic sensing and monitoring. The resonant nature of these echoes is particularly important, as it allows for species identification and size estimation. In this review, we focus on the resonance structure of fish echoes, summarizing key findings from previous studies and presenting a framework for modeling multiscattering effects.

Background

Previous research has demonstrated that fish echoes exhibit resonant behavior due to the swimbladder's ability to reflect sound waves (Holliday, 1972; Love, 1978; McCartney et al., 1971). The resonance frequency is influenced by the swimbladder's size and shape, as well as the surrounding fluid environment. Understanding the resonance structure of fish echoes is crucial for developing effective detection and classification methods.

Resonance Modeling

Numerous models have been proposed to describe the resonance behavior of fish echoes (Gastauer et al., 2016; Hazen & Horne, 2004; Horne et al., 2000). These models can be broadly classified into two categories: analytical and numerical. Analytical models rely on simplified assumptions about the swimbladder's shape and size, whereas numerical models employ more complex geometries and fluid dynamics.

Analytical Models

Analytical models for simple shapes, such as spheres or cylinders, have been developed to describe fish echoes (Best, n.d.). These models are useful for understanding the basic principles of resonance but may not accurately capture the complexity of real-world scenarios. More advanced analytical models, like the Kirchhoff-approximation (KRM) and Kirchhoff-ray-mode (KRMS), have been proposed to account for the swimbladder's shape and size (Macaulay et al., 2013).

Numerical Models

Numerical models, such as boundary element methods (BEM) or finite element methods (FEM), can be used to simulate the resonance behavior of fish echoes with greater accuracy. These models are particularly useful for complex geometries and fluid dynamics, which may not be easily captured by analytical models.

Multiscattering Modeling

In reality, fish echoes often involve multiple scattering events due to the presence of bubbles or other objects. To account for these effects, we propose a framework that incorporates multiscattering concepts (e.g., simple linear addition, bubble dynamics). This approach can be implemented using numerical models like BEM or FEM.

This review has provided an overview of the resonance structure in fish echoes and discussed various modeling approaches. We have also presented a novel framework for modeling multiscattering effects, which can be used to improve detection and classification methods. Future research should focus on developing more sophisticated models that account for real-world complexities, such as fluid dynamics and swimbladder shape variations.

References

Gastauer, S., Scoulding, B., Fässler, S. M., Benden, D. P., & Parsons, M. (2016). Target strength estimates of red emperor (Lutjanus sebae) with Bayesian parameter calibration. Aquatic Living Resources, 29(3), 301.

Hazen, E. L., & Horne, J. K. (2004). Comparing the modelled and measured target-strength variability of walleye pollock, Theragra chalcogramma. ICES Journal of Marine Science, 61(3), 363-377.

Horne, J. K., Walline, P. D., & Jech, J. M. (2000). Comparing acoustic model predictions to in situ backscatter measurements of fish with dual-chambered swimbladders. Journal of Fish Biology, 57(5), 1105-1121.

Horne, J. K. (2003). The influence of ontogeny, physiology, and behaviour on the target strength of walleye pollock (Theragra chalcogramma). Ices Journal of Marine Science, 60(5), 1063-1074.

Macaulay, G. J., Peña, H., Fässler, S. M., Pedersen, G., & Ona, E. (2013). Accuracy of the Kirchhoff-approximation and Kirchhoff-ray-mode fish swimbladder acoustic scattering models. PloS one, 8(5), e64055.

Note

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