In ocean bottom node (OBN) seismic exploration, data inaccuracies primarily stem from various factors such as multicomponent crosstalk, poor coupling, gain imbalance, inconsistent frequency response, and geophone orientation deviation. Among these, the orientation deviation of geophones is a critical factor affecting data quality. Influenced by seabed topography, ocean currents, and free-fall deployment, the actual orientation of three-component geophones often deviates from the designed layout. This misalignment leads to uneven energy distribution of seismic wavefields across the X, Y, and Z components, thereby degrading wavefield reception accuracy and imaging performance. To address this, the primary task in multicomponent seismic data processing is geophone reorientation. This involves estimating the actual orientation angles either by using built-in inclinometers or by applying data-driven approaches, and then applying a rotation matrix to align the recorded data from the acquisition coordinate system to the designed coordinate system. This paper proposes a geophone orientation correction method based on the Leader Harris Hawks Optimization (LHHO) algorithm. By analyzing the energy distribution characteristics of first-arrival waves in three-component records, the method accurately estimates the geophone orientation. Furthermore, by integrating the projection of first-arrival polarization vectors in the XOZ plane with the correlation between the P and Z components, the method effectively constrains the multi-solution problem caused by coordinate axis reversal. Experimental results indicate that, in the absence of direct wave signals in shallow-water OBN environments, the proposed method demonstrates high computational efficiency and accuracy.

This paper proposes an attention-based Multi-level wavelet Convolutional Neural Network to suppress free surface multiples in marine seismic data. Wavelet transform is used to compress the feature size of image data to avoid the loss of information caused by traditional down-sampling. Besides, it also introduces an attention mechanism to expand its receptive field and improve the fidelity of training. The algorithm proposed in this paper is compared with DnCNN network and U-Net network to test the simulated data and actual data under different observation modes. The experimental results show that the attention Mechanism in MWCNN can better separate the primary wave and the free surface multiple, and the protection of the effective signal are better than the other two networks. It has strong generalization ability and suppression efficiency.