Reconstruction of randomly missing seismic data using XGBoost optimized by hybrid particle swarm-grey wolf algorithm

To address irregular missing seismic data caused by environmental interference during field acquisition, this paper proposes a local learning-based reconstruction method that integrates hybrid particle swarm optimization and grey wolf optimizer (HPSOGWO) algorithm with the XGBoost model. The proposed method establishes a nonlinear mapping relationship between seismic trace spatial coordinates (trace number and sampling number) and amplitude values. By adaptively optimizing feature windows using the HPSOGWO algorithm, this method achieves intelligent selection of adjacent trace data and high-precision prediction of missing values. Compared with the traditional curvelet-transform-based projection onto convex sets (POCS), the proposed approach significantly improves reconstruction accuracy in complex structural areas. In contrast to deep learning methods such as U-Net, it reduces the reliance on large training datasets and lowers computational costs. Tests on a three-layer horizontal layered model with 20% random missing traces show that the proposed method achieves a peak signal-to-noise ratio (PSNR) improvement of 11 dB over Curvelet-POCS and 7 dB over U-Net. F-K spectrum analysis further confirms its effectiveness in preserving seismic wavefield characteristics in the frequency domain. Tests on real onshore 2D seismic data show that the reconstructed profile with 20% missing traces achieves a relative amplitude error of 5.72%, demonstrating high amplitude fidelity and phase consistency. The method thus provides an effective and practical solution for seismic data reconstruction under complex geological conditions.