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.

Conventional post-stack seismic data are usually affected by absorption attenuation，resulting ina lower peak frequency，narrower frequency band，low resolution，and poor inversion prediction effect. Therefore，an improved Q estimation and inverse Q -filtering method is proposed to improve the resolution of seismic data and obtain the fidelity and amplitude -preserving seismic data. Firstly，in Q estimation，given the problem that the extracted wavelet amplitude spectrum deviates from the actual situation due to the thin -layer tuning effect that affects the Q estimation accuracy，the complete ensemble empirical model decomposition with adaptive noise（CEEMDAN）method is introduced，which eliminates the interference of reflection coefficients by decomposing and reconstructing the log amplitude spectrum and thus obtains the wavelet amplitude spectrum that removes the tuning effect. Combined with the centroid frequency shift of the energy spectrum，higher-accuracy Q values are obtained. Then，in terms of inverse Q filtering，the amplitude compensation function of the time-varying gain stabilization factor method is optimized to overcome the density dependence and obtain more stable inverse Q -filtering results. The actual data processing results show that the proposed method can obtain fidelity and amplitude-preserving high -resolution post-stack seismic data，which lays a reliable data foundation for subsequent exploration and development of the study area.

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.