High-Density Seismic Signal Processing Methods for the Gongshanmiao 3D Oil Survey of the Lianggaoshan Formation in the Sichuan Basin: A Case Study

The Lianggaoshan Formation in the Gongshanmiao block of the Sichuan Basin is characterized by narrow channel sand bodies and thin layers, resulting in weak seismic responses, which leads to poor identification of small-scale fault–fracture systems. Initial high-density 3D seismic data exhibit strong shallow surface waves and significant shot-to-shot variations in energy and frequency, necessitating amplitude-preserving noise attenuation and broadband wavelet consistency processing. First, pre-stack multi-domain amplitude-preserving noise attenuation is applied, integrating surface-wave forward modeling, stationary wavelet transform, and matrix singular value decomposition to suppress complex noise. Next, robust deconvolution constrained by a target wavelet improves broadband consistency. Subsequently, anisotropic depth-domain velocity modeling and imaging under rugged topography are conducted using a well-constrained TTI initial velocity model and full-azimuth angle-domain grid tomography. Compared with conventional data, the processed high-density data significantly enhance bandwidth, structural imaging, and thin-layer resolution. Imaging continuity of small faults (6–10 m throw) is markedly improved, and the channel characterization accuracy of the Liang-2 Member increases from 180 m to 60 m. This workflow delivers high-SNR, high-resolution, and high-fidelity results, providing a reliable basis for thin-sandbody prediction and reservoir evaluation.