Machine Learning-Based Bias Correction Method for Ocean Buoy Sensor Observations

Zhong Guorong; Li Xuegang; Song Jinming; Qu Baoxiao; Ma Jun; Yuan Huamao; Duan Liqin

doi:10.12284/hyxb2025000

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Zhong Guorong，Li Xuegang，Song Jinming, et al. Machine Learning-Based Bias Correction Method for Ocean Buoy Sensor Observations[J]. Haiyang Xuebao，2025, 47(x)：1–9 doi: 10.12284/hyxb2025000

Citation:

Zhong Guorong，Li Xuegang，Song Jinming, et al. Machine Learning-Based Bias Correction Method for Ocean Buoy Sensor Observations[J]. Haiyang Xuebao，2025, 47(x)：1–9 doi: 10.12284/hyxb2025000

Citation:

Zhong Guorong，Li Xuegang，Song Jinming, et al. Machine Learning-Based Bias Correction Method for Ocean Buoy Sensor Observations[J]. Haiyang Xuebao，2025, 47(x)：1–9 doi: 10.12284/hyxb2025000

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Machine Learning-Based Bias Correction Method for Ocean Buoy Sensor Observations

doi: 10.12284/hyxb2025000

Zhong Guorong^{1, 2, 3, 4
,},
Li Xuegang^{1, 2, 3, 4
,
,},
Song Jinming^{1, 2, 3, 4},
Qu Baoxiao^{1, 2, 3},
Ma Jun^{1, 2, 3},
Yuan Huamao^{1, 2, 3},
Duan Liqin^{1, 2, 3}

1.
Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
2.
Laoshan Laboratory, Qingdao 266237, China
3.
University of Chinese Academy of Sciences, Beijing 101407, China
4.
Ocean Big Data Center, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

Received Date: 2025-07-08
Rev Recd Date: 2025-08-13

Available Online: 2025-08-21

Abstract

Abstract

Ocean buoy observations serve as a vital means of acquiring data for marine research. However, direct measurements from buoys are subject to significant biases induced by factors such as sensor baseline drift, biofouling, and seawater corrosion, necessitating rigorous bias correction to ensure data reliability. While numerous quality control (QC) schemes for physical oceanographic parameters from buoy data have been extensively studied and reported, robust and practical sensor QC measures for more complex and variable chemical parameters remain lacking. To address this gap, this study analyzed 90-day laboratory monitoring data for parameters including dissolved oxygen, chlorophyll concentration, pH, and partial pressure of CO₂ (pCO₂). The analysis revealed that the drift bias in these monitored parameters exhibits a strong linear correlation with fundamental sensor parameters such as conductivity and sensor output voltage and with biological factors to varying degrees. Building upon these findings, we developed a drift bias correction method based on machine learning algorithm to fit the nonlinear relationships between drift bias and fundamental sensor parameters. This method effectively validates buoy sensor data for chemical parameters. Application of this method to observational data across different parameters significantly reduces the deviation between drifted data and true values. It thus provides a novel QC approach for achieving sustained, stable, and high-quality acquisition of marine chemical parameter data from buoy observations.
- monitoring data,
- drift deviation,
- quality control,
- machine learning,
- buoy observation

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References(25)

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