Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species
To acoustically assess the biomass of multiple species or taxa within a survey region, the volume backscatter data should be apportioned to the constituent sound scatterers. Typically, measured backscatter is attributed to certain species using predictions at different frequencies, mostly based on t...
Published in: | Deep Sea Research Part I: Oceanographic Research Papers |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | http://hdl.handle.net/10508/9968 http://hdl.handle.net/10261/318051 https://doi.org/10.1016/j.dsr.2016.01.006 |
id |
ftcsic:oai:digital.csic.es:10261/318051 |
---|---|
record_format |
openpolar |
spelling |
ftcsic:oai:digital.csic.es:10261/318051 2024-02-11T10:05:44+01:00 Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species Peña, Marian Calise, Lucio 2016 http://hdl.handle.net/10508/9968 http://hdl.handle.net/10261/318051 https://doi.org/10.1016/j.dsr.2016.01.006 en eng #PLACEHOLDER_PARENT_METADATA_VALUE# MAFIA, SCAPA Centro Oceanográfico de Baleares AM Deep-Sea Research Part I-Oceanographic Research Papers, 2,4210, 100. 2016: 50-64 0967-0637 http://hdl.handle.net/10508/9968 http://hdl.handle.net/10261/318051 doi:10.1016/j.dsr.2016.01.006 open Centro Oceanográfico de Baleares Medio Marino research article 2016 ftcsic https://doi.org/10.1016/j.dsr.2016.01.006 2024-01-16T11:44:59Z To acoustically assess the biomass of multiple species or taxa within a survey region, the volume backscatter data should be apportioned to the constituent sound scatterers. Typically, measured backscatter is attributed to certain species using predictions at different frequencies, mostly based on the difference in scattering at the frequencies of 38 and 120 kHz ('dual frequency method'). We used the full version of the stochastic distorted wave Born approximation model (SDWBA) to predict backscatter spectra for Meganyctiphanes norvegica and to explore the sensitivities of ΔMVBS to the model parameters, e.g. acoustic frequency and incidence angle, and animal density and sound speed contrast, length, and shape. The orientation is almost the unique parameter responsible for variation, with fatness affecting longer lengths. We present a summary of ΔMVBS that can serve as the basis for identification algorithms. Next, we simulate the scenario encountered in the Balearic Sea (western Mediterranean) where Northern krill are mixed with mesopelagic fish species (bristlemouths and lanternfishes), which are modeled with a prolate spheroid model. Simulated numerical data are employed to emulate the discrimination process with the most common identification techniques and typical survey frequencies. The importance of using density-independent techniques for acoustic classification is highlighted. Finally, an unsupervised neural network, the Self-Organizing Map (SOM), is used to cluster these theoretical data and identify the frequencies that provide, in this case, the most classification potential. The simulation results confirm that pairs of frequencies spanning the Rayleigh and geometric scattering regimes of the targets are the most useful for clustering; a minimum of four frequencies are necessary to separate the three species, while three frequencies are able to differentiate krill from mesopelagic fish species. Article in Journal/Newspaper Meganyctiphanes norvegica Northern krill Digital.CSIC (Spanish National Research Council) Deep Sea Research Part I: Oceanographic Research Papers 110 50 64 |
institution |
Open Polar |
collection |
Digital.CSIC (Spanish National Research Council) |
op_collection_id |
ftcsic |
language |
English |
topic |
Centro Oceanográfico de Baleares Medio Marino |
spellingShingle |
Centro Oceanográfico de Baleares Medio Marino Peña, Marian Calise, Lucio Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species |
topic_facet |
Centro Oceanográfico de Baleares Medio Marino |
description |
To acoustically assess the biomass of multiple species or taxa within a survey region, the volume backscatter data should be apportioned to the constituent sound scatterers. Typically, measured backscatter is attributed to certain species using predictions at different frequencies, mostly based on the difference in scattering at the frequencies of 38 and 120 kHz ('dual frequency method'). We used the full version of the stochastic distorted wave Born approximation model (SDWBA) to predict backscatter spectra for Meganyctiphanes norvegica and to explore the sensitivities of ΔMVBS to the model parameters, e.g. acoustic frequency and incidence angle, and animal density and sound speed contrast, length, and shape. The orientation is almost the unique parameter responsible for variation, with fatness affecting longer lengths. We present a summary of ΔMVBS that can serve as the basis for identification algorithms. Next, we simulate the scenario encountered in the Balearic Sea (western Mediterranean) where Northern krill are mixed with mesopelagic fish species (bristlemouths and lanternfishes), which are modeled with a prolate spheroid model. Simulated numerical data are employed to emulate the discrimination process with the most common identification techniques and typical survey frequencies. The importance of using density-independent techniques for acoustic classification is highlighted. Finally, an unsupervised neural network, the Self-Organizing Map (SOM), is used to cluster these theoretical data and identify the frequencies that provide, in this case, the most classification potential. The simulation results confirm that pairs of frequencies spanning the Rayleigh and geometric scattering regimes of the targets are the most useful for clustering; a minimum of four frequencies are necessary to separate the three species, while three frequencies are able to differentiate krill from mesopelagic fish species. |
format |
Article in Journal/Newspaper |
author |
Peña, Marian Calise, Lucio |
author_facet |
Peña, Marian Calise, Lucio |
author_sort |
Peña, Marian |
title |
Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species |
title_short |
Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species |
title_full |
Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species |
title_fullStr |
Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species |
title_full_unstemmed |
Use of SDWBA predictions for acoustic volume backscattering and the Self-Organizing Map to discern frequencies identifying Meganyctiphanes norvegica from mesopelagic fish species |
title_sort |
use of sdwba predictions for acoustic volume backscattering and the self-organizing map to discern frequencies identifying meganyctiphanes norvegica from mesopelagic fish species |
publishDate |
2016 |
url |
http://hdl.handle.net/10508/9968 http://hdl.handle.net/10261/318051 https://doi.org/10.1016/j.dsr.2016.01.006 |
genre |
Meganyctiphanes norvegica Northern krill |
genre_facet |
Meganyctiphanes norvegica Northern krill |
op_relation |
#PLACEHOLDER_PARENT_METADATA_VALUE# MAFIA, SCAPA Centro Oceanográfico de Baleares AM Deep-Sea Research Part I-Oceanographic Research Papers, 2,4210, 100. 2016: 50-64 0967-0637 http://hdl.handle.net/10508/9968 http://hdl.handle.net/10261/318051 doi:10.1016/j.dsr.2016.01.006 |
op_rights |
open |
op_doi |
https://doi.org/10.1016/j.dsr.2016.01.006 |
container_title |
Deep Sea Research Part I: Oceanographic Research Papers |
container_volume |
110 |
container_start_page |
50 |
op_container_end_page |
64 |
_version_ |
1790602895707602944 |