Near-Surface Structural Regularization for Full-Waveform Inversion Using Directional Total Variation

Full-waveform inversion (FWI) is increasingly used in land seismic exploration to achieve high-resolution near-surface models. In complex near-surface environments, however, FWI is challenged by noisy data and inaccurate initial models, raising the need for an effective regularization strategy to mitigate the inherent ill-posedness of FWI. Incorporating geological information, such as structural dips, into the regularization operator, has proven effective in stabilizing FWI and promoting geologically meaningful results. Obtaining clear seismic images that provide structural insights is, nonetheless often hindered in complex near-surface surveys due to noisy reflection data and gradient weathering velocity. Structural regularization treats imaged reflectors as velocity contours and penalizes velocity variations along the dips. We propose a novel approach involving deformable-layer tomography (DLT) to directly invert for velocity contour distributions. DLT is well suited for near-surface applications as it accommodates both gradient velocity variations and abrupt velocity contrasts. In order to avoid erroneous structural regularization, we evaluate the accuracy of the DLT-estimated dips and assign weights accordingly. The weighted dip field is used to construct directional total variation (TV) in regularizing FWI, using the edge-preserving smoothing of TV regularization as well as the dip constraint. Using a realistic near-surface model, we demonstrate that FWI with the DLT-guided directional TV regularization outperforms conventional TV regularization. Our findings underscore the advantages of incorporating geologic structural constraint into FWI under complex near-surface conditions, highlighting the improved fidelity and resolution in near-surface velocity model building.