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 ratio (S/N) 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 post-critical 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.