Performance Analysis of an Ice-Based Buoy Operating from the Packed Ice Zone to the Marginal Ice Zone with an Imaging System
Abstract
1. Introduction
- (1)
- A novel imaging-based buoy system with a titanium alloy housing and sapphire window was proposed and field-validated, capable of stable operation at −40 °C in complex under-ice environments and continuously capturing under-ice imagery for three months, addressing the lack of imaging in traditional buoys;
- (2)
- The application of the ELM algorithm significantly improved battery state prediction accuracy (RMSE = 0.05, MAE = 0.187), providing reliable energy assurance for long-term polar buoy operation;
- (3)
- Low-temperature imaging, intelligent battery prediction, and wave energy-assisted power supply were integrated into a single buoy platform, forming a low-cost, long-term, and autonomous polar observation system with potential for multi-scenario applications.
2. Description of the Ice-Based Buoy with Imaging System
2.1. System Composition
2.2. Design of Ice-Based Buoy with Imaging System
2.2.1. Main Buoy
2.2.2. Image System
- (1)
- Good sealing and high material strength;
- (2)
- It has corrosion resistance and can ensure the long-term operation of the camera in seawater;
- (3)
- The front observation window lens is thin and has strong light transmission ability, with low light energy loss.
2.3. Analysis of Buoy Load
3. State Estimation of Battery of Ice-Based Buoy Based on ELM
- Random Initialization: The weights W from the input layer to the hidden layer and the bias b are set randomly.
- Calculate Hidden Layer Output: For each input sample matrix X, the hidden layer output matrix H is computed using the formula as follows:
- Compute Output Layer Weights: The output layer weights β are solved using the least squares method as follows:
- Prediction: For the input sample matrix Xnew, the hidden layer output matrix Hnew is calculated as:
4. Experimental Results
4.1. Experiment on Indoor Wave-Current Flume
4.2. Image Acquisition Experiment in the Arctic Ocean
5. Conclusions and Future Work
5.1. Experimental and Simulation Results
- Laboratory wave-tank tests demonstrated that the buoy can operate stably under mild wave conditions, with both the imaging system and the energy-harvesting module showing reliable performance;
- A three-month Arctic field deployment further confirmed the feasibility of the buoy in extreme low-temperature environments;
- Numerical simulations exhibited consistent trends with the experimental observations, verifying buoy stability and energy-harvesting performance under different wave conditions.
5.2. Limitations
- The laboratory tests were conducted under mild wave conditions and cannot fully represent Arctic storm environments;
- The imaging resolution was insufficient for detailed ecological classification under ice;
- Battery lifetime and energy management strategies have not yet been validated on a year-round scale.
5.3. Future Improvements
- Conduct tests under more energetic wave conditions (e.g., in large-scale facilities or offshore test sites) to further assess performance in extreme environments;
- Enhance the imaging system with higher resolution and better low-light performance;
- Extend the duration of field deployments and adopt more advanced energy management and prediction algorithms to support year-round continuous operation.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Acronym/Symbol | Description |
GNSS | Global Navigation Satellite System |
GPS | Global Positioning System |
MIZ | Marginal Ice Zone |
ELM | Extreme Learning Machine |
LSTM | Long Short-Term Memory |
BiLSTM | Bidirectional Long Short-Term Memory |
SAELSTM | Self-Attention Long Short-Term Memory |
BP | Back Propagation Neural Network |
RF | Random Forest |
RMSE | Root Mean Square Error |
MAE | Mean Absolute Error |
ρ | Density of fluid |
C_D/C_L | Drag coefficient/Lift coefficient |
A | Projected area |
V | Velocity |
F | Force |
T | Period/Time |
H | Wave height |
R | Radius |
M | Mass |
g | Gravitational acceleration |
Vol | Submerged volume |
θ | Incident angle/Direction angle |
References
- Screen, J.; Simmonds, I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature 2010, 464, 1334–1337. [Google Scholar] [CrossRef]
- Kwok, R.; Rothrock, D.A. Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008. Geophys. Res. Lett. 2009, 36, L15501. [Google Scholar] [CrossRef]
- Rothrock, D.; Percival, D.; Wensnahan, M. The decline in arctic sea-ice thickness: Separating the spatial, annual, and interannual variability in a quarter century of submarine data. J. Geophys. Res. Ocean. 2008, 113, C05003. [Google Scholar] [CrossRef]
- Lei, R.B.; Li, Z.J.; Cheng, Y.F. A new apparatus for monitoring sea ice thickness based on the magnetostrictive–delay–line principle. J. Atmos. Ocean. Technol. 2009, 26, 818–827. [Google Scholar] [CrossRef]
- Laxon, S.; Peacock, N.; Smith, D. High interannual variability of sea ice thickness in the Arctic region. Nature 2003, 425, 947–950. [Google Scholar] [CrossRef] [PubMed]
- Polashenski, C.; Perovich, D.; Courville, Z. The mechanisms of sea ice melt pond formation and evolution. J. Geophys. Res. Ocean. 2012, 117, C01001. [Google Scholar] [CrossRef]
- Flocco, D.; Feltham, D.; Bailey, E.; Schroeder, D. The refreezing of melt ponds on Arctic sea ice. J. Geophys. Res. Ocean. 2015, 120, 647–659. [Google Scholar] [CrossRef]
- Webster, M.; Rigor, I.; Perovich, D.; Richter-Menge, A.; Polashenski, C.; Light, B. Seasonal evolution of melt ponds on Arctic sea ice. J. Geophys. Res. Ocean. 2015, 120, 5968–5982. [Google Scholar] [CrossRef]
- Polashenski, C.; Golden, K.; Perovich, D.; Skyllingstad, E.; Arnsten, A.; Stwertka, C.; Wright, N. Percolation blockage: A process that enables melt pond formation on first year Arctic sea ice. J. Geophys. Res. Ocean. 2017, 122, 413–440. [Google Scholar] [CrossRef]
- Lei, R.B.; Cheng, B.; Heil, P. Seasonal and Interannual Variations of Sea Ice Mass Balance from the Central Arctic to the Greenland Sea. J. Geophys. Res. Ocean. 2018, 123, 2422–2439. [Google Scholar] [CrossRef]
- Lei, R.; Li, N.; Heil, P. Multiyear sea ice thermal regimes and oceanic heat flux derived from an ice mass balance buoy in the Arctic Ocean. J. Geophys. Res. Ocean. 2014, 119, 537–547. [Google Scholar] [CrossRef]
- Strong, C.; Rigor, I. Arctic marginal ice zone trending wider in summer and narrower in winter. Geophys. Res. Lett. 2013, 40, 4864–4868. [Google Scholar] [CrossRef]
- Notz, D.; Community, S. Arctic sea ice in CMIP6. Geophys. Res. Lett. 2020, 47, e2019GL086749. [Google Scholar] [CrossRef]
- Itkin, P.; Spreen, G.; Hvidegaard, S.; Skourup, H.; Gerland, S.; Wilkinson, J.; Granskog, M. Contribution of deformation to sea ice mass balance: A case study from an N-ICE2015 storm. Geophys. Res. Lett. 2018, 45, 789–796. [Google Scholar] [CrossRef]
- Boutin, G.; Lique, C.; Ardhuin, F.; Rousset, C.; Talandier, C.; Accensi, M.; Girard-Ardhuin, F. Towards a coupled model to investigate wave–sea ice interactions in the Arctic marginal ice zone. Cryosphere 2020, 14, 709–735. [Google Scholar] [CrossRef]
- Lee, C.; Thomson, J.; The Marginal Ice Zone Team; The Arctic Sea State Team. An autonomous approach to observing the seasonal ice zone in the western Arctic. Oceanography 2017, 30, 56–68. [Google Scholar] [CrossRef]
- Jackson, K.; Wilkinson, J.; Maksym, T. A novel and low cost sea ice mass balance buoy. J. Atmos. Ocean. Technol. 2013, 30, 13825. [Google Scholar] [CrossRef]
- Kim, J.; Moon, W.; Wells, A.; Wilkinson, J.; Langton, T.; Hwang, B.; Granskog, M.; Jones, D. Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice. Geophys. Res. Lett. 2018, 45, 8304–8313. [Google Scholar] [CrossRef]
- Doble, M.; Wadhams, P. Dynamical contrasts between pancake and pack ice, investigated with a drifting buoy array. J. Geophys. Res. Ocean. 2006, 111, C11S24. [Google Scholar] [CrossRef]
- Roach, L.; Smith, M.; Dean, S. Quantifying growth of pancake sea ice floes using images from drifting buoys. J. Geophys. Res. Ocean. 2018, 123, 2851–2866. [Google Scholar] [CrossRef]
- Rabault, J.; Taelman, C.; Idžanović, M.; Hope, G.; Nose, T.; Kristoffersen, Y.; Jensen, A.; Breivik, Ø.; Bryhni, H.T.; Hoppmann, M.; et al. A position and wave spectra dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023. Sci. Data. 2024, 11, 1417. [Google Scholar] [CrossRef]
- Müller, M.; Rabault, J.; Abdel-Fattah, D.; Sutherland, G. Distributed observation networks in the Arctic Marginal Ice Zone to advance forecasting systems. Bull. Am. Meteorol. Soc. 2025, 106, E1204–E1210. [Google Scholar] [CrossRef]
- Zhu, W.; Liu, S.; Xu, S.; Zhou, L. A 12-year climate record of wintertime wave-affected marginal ice zones in the Atlantic Arctic based on CryoSat-2. Earth Syst. Sci. Data 2023, 16, 2917–2940. [Google Scholar] [CrossRef]
- Wahlgren, S.; Thomson, J.; Sutherland, G.; Stopa, J.E.; Lund, B.; Collins, C.O. Direct Observations of Wave–Sea Ice Interactions in the Antarctic Marginal Ice Zone. J. Geophys. Res. Ocean. 2023, 128, e2023JC019948. [Google Scholar] [CrossRef]
- Lund, B.; Graber, H.; Persson, P.; Smith, M.; Doble, M.; Thomson, J.; Wadhams, P. Arctic sea ice drift measured by shipboard marine radar. J. Geophys. Res. Ocean. 2018, 123, 4298–4321. [Google Scholar] [CrossRef]
- Lee, S. An efficient content-based image enhancement in the compressed domain using Retinex theory. IEEE Trans. Circuits Syst. Video Technol. 2007, 17, 199–213. [Google Scholar] [CrossRef]
1: Start Procedure | ||
2: Initialization | ||
| ||
| ||
| ||
3: Photography process | ||
| ||
| ||
| ||
| ||
| ||
4: End Procedure |
Algorithm | MAE | RMSE | |
---|---|---|---|
Training Set | ELM | 0.1907 | 0.0577 |
LSTM | 0.1825 | 0.0517 | |
BP | 0.1920 | 0.0570 | |
BiLSTM | 0.1849 | 0.0020 | |
SAELSTM | 0.1797 | 0.0501 | |
RF | 0.1745 | 0.0504 | |
Test Set | ELM | 0.1868 | 0.0588 |
LSTM | 0.1865 | 0.0602 | |
BP | 0.1878 | 0.0617 | |
BiLSTM | 0.1894 | 0.0635 | |
SAELSTM | 0.1909 | 0.0625 | |
RF | 0.2584 | 0.0803 |
Parameter | Performance |
---|---|
Length | 75 m |
Width | 1.8 m |
Height | 2 m |
Maximum test water depth | 1.5 m |
Maximum flow rate | 0.8 m3/s |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zuo, G.; Huang, H.; Chen, H. Performance Analysis of an Ice-Based Buoy Operating from the Packed Ice Zone to the Marginal Ice Zone with an Imaging System. J. Mar. Sci. Eng. 2025, 13, 1717. https://doi.org/10.3390/jmse13091717
Zuo G, Huang H, Chen H. Performance Analysis of an Ice-Based Buoy Operating from the Packed Ice Zone to the Marginal Ice Zone with an Imaging System. Journal of Marine Science and Engineering. 2025; 13(9):1717. https://doi.org/10.3390/jmse13091717
Chicago/Turabian StyleZuo, Guangyu, Haocai Huang, and Huifang Chen. 2025. "Performance Analysis of an Ice-Based Buoy Operating from the Packed Ice Zone to the Marginal Ice Zone with an Imaging System" Journal of Marine Science and Engineering 13, no. 9: 1717. https://doi.org/10.3390/jmse13091717
APA StyleZuo, G., Huang, H., & Chen, H. (2025). Performance Analysis of an Ice-Based Buoy Operating from the Packed Ice Zone to the Marginal Ice Zone with an Imaging System. Journal of Marine Science and Engineering, 13(9), 1717. https://doi.org/10.3390/jmse13091717