In ocean bottom node（OBN）seismic exploration，significant discrepancies often exist between the actual node positions and the initial surveyed locations due to factors such as ocean currents，tides，topographic variations，and fishing vessel dragging，necessitating secondary positioning. To address this，a high-precision secondary positioning method based on four-quadrant stacking is proposed. Specifically，within a relative coordi- nate system，shot points are divided into four quadrants according to their azimuth. Linear moveout correction and common receiver point stacking are performed separately for shot points in each quadrant. Subsequently， the displacement of the node from the surveyed position to the actual position is decomposed into two mutually perpendicular components. These two components are determined by analyzing the first arrival time differences from the common receiver points in each quadrant，which thereby enables precise estimation of the node’s ac- tual coordinates. Application to field data demonstrates that this method achieves high -precision secondary posi- tioning of nodes. Moreover，the four -quadrant stacking strategy significantly improves the reliability of first ar- rival picking. Compared with traditional methods，this approach offers higher positioning accuracy and effi- ciency，showing excellent engineering applicability and promising potential for broader adoption.

Channel identification is crucial for predicting fluvial facies reservoirs. However，when the P-wave impedance contrast between channel sandstones and surrounding rocks is minimal，it is difficult to use only poststack P -wave seismic data for channel identification. S -wave data can effectively enhance the reliability of predicting the spatial distribution of channels. However，the combined identification process of P-wave and S-wave involves challenges such as difficult parameter selection，high subjectivity，and extended working cycles， leading to inefficiencies and reduced reliability. This paper proposes an automatic channel identification methodbased on the joint P-wave and S-wave seismic data. First，to address the issue of insufficient sample data，it puts forward a method for automatically generating synthetic forward modeling samples of 3D channel geological models based on actual data interpretation and channel interpretation results，effectively expanding the sample data set. Subsequently，a new 3D automatic channel identification network structure is then designed， which effectively integrates P-wave and S-wave seismic data，enhancing the reliability of the identification results. Finally，the proposed method is applied to identify tight gas channel sandstones in a work area in southwestern China. Compared with traditional seismic attribute analysis and intelligent identification results relying on a single data type，the proposed method demonstrates higher efficiency and reliability，validating its applicability.

Intelligent seismic velocity inversion is currently a hot and challenging topic in seismic exploration research. Nevertheless, the complex structure of deep learning networks demands significant computing power from hardware devices, which restricts the application of the model in scenarios with large data volumes and high timeliness requirements. To address these practical issues, in this paper, the U-Net is improved based on the concepts of feature engineering and model lightweighting, and the inversion networks U-Net vG for GPU and U-Net vC for CPU are proposed. Firstly, the characteristics of the velocity inversion network are analyzed to deduce the lightweighting principles of convolutional neural networks. Subsequently, lightweight processing is conducted on the multi-scale module, attention gate module, and feature extraction module to obtain a lightweight convolutional neural network for velocity modeling, which reduces the network volume while maintaining prediction accuracy. Data test results demonstrate that the training process of the proposed network has lower requirements for high-performance hardware resources, and that the network enables efficient velocity inversion, possesses higher seismic velocity inversion accuracy, and exhibits superior noise resistance. It provides a new idea for solving the computing power bottleneck problem in seismic data inversion.