Neural network with signal parameters featuring for near‐surface velocity model building

Abstract This study presents a method that integrates spectral recomposition (SR) with a neural network to improve near‐surface seismic analysis. The approach incorporates SR‐derived wavelet‐timing attributes into a fully convolutional network (FCN) to enhance the characterization of shallow subsurface structures. Field evaluation was conducted using S‐wave reflection data acquired near Rotterdam (the Netherlands). The results show good agreement with prior geological interpretations of key stratigraphic interfaces, including the water table (∼1 m), alternating Holocene clay–sand layers (2–12 m) and the stiff Pleistocene sand boundary (22–25 m). Compared with conventional approaches, the FCN trained with SR‐derived features reduced depth‐prediction errors and improved the continuity of interpreted interfaces, particularly in complex near‐surface conditions. These results indicate that incorporating SR‐derived signal parameters can enhance the robustness and reliability of neural‐network‐based near‐surface velocity model building, although further validation across different geological settings is required.