Interoperable and scalable echosounder data processing with Echopype
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866908354563014656 |
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| author | Lee, Wu-Jung Setiawan, Landung Tuguinay, Caesar Mayorga, Emilio Staneva, Valentina |
| author_facet | Lee, Wu-Jung Setiawan, Landung Tuguinay, Caesar Mayorga, Emilio Staneva, Valentina |
| contents | Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype's modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_00187 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Interoperable and scalable echosounder data processing with Echopype Lee, Wu-Jung Setiawan, Landung Tuguinay, Caesar Mayorga, Emilio Staneva, Valentina Signal Processing Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype's modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies. |
| title | Interoperable and scalable echosounder data processing with Echopype |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2111.00187 |