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June 10, 2026Intelligent Marine Technology and SystemsOpen Access

Time-lag correction for oceanic in situ CO2 sensors using a physics-informed hybrid BiLSTM-attention and Kalman filter framework

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Authors

MDMengtie DuMLMeng LiXWXuezhu Wang

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Overview

Randomized trial demonstrates time-lag correction in oceanic sensors, suggesting enhanced data accuracy.

Key Points

  • The aim is to correct time-lag in oceanic CO2 sensors to improve data quality for carbon cycle analysis.
  • Developed a physics-informed hybrid framework combining BiLSTM and attention mechanisms.
  • Applied a Kalman filter for post-processing to reduce noise.
  • Addressed challenges such as data heterogeneity and sensor noise.
  • The framework reduces phase delay and improves system response time by over 80%.
  • Outperformed traditional models including LTI and ARX in time-lag correction.
  • Successfully minimized residual errors while suppressing high-frequency noise.

Cite This Study

Du et al. (2026) studied this question.

synapsesocial.com/papers/6a2900566f82f25be989cf69https://doi.org/10.1007/s44295-026-00106-6
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