Enhanced Offshore Wind Farm Geophysical Surveys: Shearlet-Sparse Regularization in Multi-Channel Predictive Deconvolution

Abstract

This study introduces a novel multi-channel predictive deconvolution method enhanced by Shearlet-based sparse regularization, aimed at improving the accuracy and stability of subsurface seismic imaging, particularly in offshore wind farm site assessments. Traditional multi-channel predictive deconvolution techniques often struggle with noise interference, limiting their effectiveness. By integrating Shearlet transform into the multi-channel predictive framework, our approach leverages its directional and multiscale properties to enhance sparsity and directionality in seismic data representation. Tests on both synthetic and field data demonstrate that our method not only provides more accurate seismic images but also shows significant resilience to noise, compared to conventional methods. These findings suggest that the proposed technique can substantially improve geological feature identification and has great potential for enhancing the efficiency of seabed surveys in marine renewable energy development.