1887

Abstract

The remote characterization of seafloor types through the processing of echosounder data is a useful geophysical tool, as it allows the reduction of required sediment grabbed samples within one survey area. While traditional sampling methods require ships to stop, the remote characterization solution allows more expedite data collection during ships transit. Few programs are now commercially available in the market, which enables the processing of echosounder and sidescan sonar data, producing seafloor sediment maps. Software analyzes the backscatter intensities through different approaches (eg. image texture, seabed angular response, power spectral analysis, etc) and each approach can be performed with distinct processing configurations. The varied methods (approach and processing configuration) used for data processing generates different seabed map solutions. In order to evaluate the most appropriate methods for data processing, a quantitative analysis is being performed. Then, seafloor characterization maps are being correlated to sediment grabbed samples, in order to determine their agreement. Software termed SEDIMAP has been developed in this study to automatically perform this correlation task, which is described in this article. A chosen dataset with multibeam echosounder data and sediment grabbed samples, collected inside Guanabara Bay-RJ-Brazil, will be presented. Further studies are being developed using the same correlation methodology for other datasets, using both singlebeam and sidescan sonar data, which have already been collected in the same survey area. All datasets are going to be processed with different commercial software and using distinct processing configurations. Plans are to define reliable solutions for data processing, in order to produce remote characterization sediment maps that reasonably represent the seafloor.

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/content/papers/10.3997/2214-4609-pdb.264.SBGF_2786
2011-08-15
2021-10-26
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.264.SBGF_2786
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