Sparsity-Promoting Weighted Radon Preconditioning for Land Waveform Inversion

The effectiveness of full-waveform inversion (FWI) and imaging in land seismic data is highly dependent on the data quality. However, land seismic datasets are often characterized by a low signal-to-noise (S/N) ratio due to factors, such as strong near-surface velocity contrasts, elastic effects, and subsurface heterogeneity, which significantly hinder the accuracy of both inversion and imaging processes. In this study, we propose a sparsity-promoting weighted Radon preconditioning method to enhance the quality of early-arriving seismic signals for FWI. We then employ demigration using an extended-imaging condition to improve the reflection quality for land seismic imaging. The proposed method combines a scanning-based approach for isolating early arrivals with a semblance-weighted sparse Radon transform. The scanning technique automatically guides early arrival identification based on maximum stacking energy and linear velocity assumption. The semblance-weighted sparse Radon transform is then applied to enhance the coherence of diving and postcritical early arrivals used in FWI. Numerical tests on both synthetic and field datasets demonstrate that our method significantly improves the coherency and S/N of early arrivals, as well as the quality of low-frequency components. These improvements in early arrivals enable FWI to generate a high-fidelity near-surface velocity model with enhanced resolution for land seismic data. Furthermore, demigration using extended-imaging condition also greatly enhances the reflection coherency and suppresses linear noise interfering with reflections, leading focused seismic images of land data.