Efficient 3D real-time adaptive AUV sampling of a river plume front
The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and fr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2023.1319719/full |
| _version_ | 1797353207093002240 |
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| author | Martin Outzen Berild Yaolin Ge Jo Eidsvik Geir-Arne Fuglstad Ingrid Ellingsen |
| author_facet | Martin Outzen Berild Yaolin Ge Jo Eidsvik Geir-Arne Fuglstad Ingrid Ellingsen |
| author_sort | Martin Outzen Berild |
| collection | DOAJ |
| description | The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume. |
| first_indexed | 2024-03-08T13:27:47Z |
| format | Article |
| id | doaj.art-b118682bb3694fe6a31d2d83545df3f8 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-03-08T13:27:47Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-b118682bb3694fe6a31d2d83545df3f82024-01-17T13:08:33ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452024-01-011010.3389/fmars.2023.13197191319719Efficient 3D real-time adaptive AUV sampling of a river plume frontMartin Outzen Berild0Yaolin Ge1Jo Eidsvik2Geir-Arne Fuglstad3Ingrid Ellingsen4Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, NorwayFisheries and New Biomarine Industry, SINTEF Ocean, Trondheim, NorwayThe coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume.https://www.frontiersin.org/articles/10.3389/fmars.2023.1319719/fulladaptive samplingocean modelingautonomous underwater vehicleGaussian random fieldstochastic partial differential equationssurrogate model |
| spellingShingle | Martin Outzen Berild Yaolin Ge Jo Eidsvik Geir-Arne Fuglstad Ingrid Ellingsen Efficient 3D real-time adaptive AUV sampling of a river plume front Frontiers in Marine Science adaptive sampling ocean modeling autonomous underwater vehicle Gaussian random field stochastic partial differential equations surrogate model |
| title | Efficient 3D real-time adaptive AUV sampling of a river plume front |
| title_full | Efficient 3D real-time adaptive AUV sampling of a river plume front |
| title_fullStr | Efficient 3D real-time adaptive AUV sampling of a river plume front |
| title_full_unstemmed | Efficient 3D real-time adaptive AUV sampling of a river plume front |
| title_short | Efficient 3D real-time adaptive AUV sampling of a river plume front |
| title_sort | efficient 3d real time adaptive auv sampling of a river plume front |
| topic | adaptive sampling ocean modeling autonomous underwater vehicle Gaussian random field stochastic partial differential equations surrogate model |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2023.1319719/full |
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