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Ocean bottom nodes (OBNs) are critical for high-resolution marine seismic data acquisition, offering full-azimuth, high-fold, and multi-component recordings directly on the seafloor. However, misalignment between the node’s local coordinate system and the global reference frame can compromise subsurface imaging quality. This study introduces a novel data-driven method to estimate and correct node orientation — pitch, roll, and heading — by leveraging radial-transverse (RT) transformations of seismic data. The methodology optimises orientation parameters by minimising transverse energy while maximising radial energy using a tailored loss function. Differential Evolution (DE), a stochastic optimisation algorithm, is used to achieve precise alignment. Evaluation metrics, including radial energy, transverse energy, and vertical amplitude, validate the method’s effectiveness using seismic data from the KG-Deepwater OBN project along India’s eastern coast. Results demonstrate significant improvements in radial energy, transverse energy reduction, and vertical polarity restoration. This approach ensures accurate sensor orientation without pre-processing corrections, enhancing data quality for subsurface imaging across diverse water depths. The findings underscore the importance of reliable orientation estimation for advancing OBN seismic acquisition techniques.