Multiparameter model building for the Qiuyue structure using 4C ocean-bottom seismometer data

Thin sand-mud-coal interbedded layers and multiples caused by shallow water pose great challenges to conventional 3D multichannel seismic techniques used to detect the deeply buried reservoirs in the Qiuyue field. In 2017, a dense ocean-bottom-seismometer (OBS) acquisition program acquired a 4C data set in the East China Sea. To delineate the deep reservoir structures in the Qiuyue field, we have applied a full-wavelbrrn inversion (FWI) workflow to this dense 4C OBS data set. After preprocessing, including receiver geometry correction. moveout correction. component rotation, and energy transformation from three dimensions to two dimensions, preconditioned first-arrival traveltime tomography based on an improved scattering-integral algorithm is applied to construct an initial P-wave velocity model. To eliminate the influence of the wavelet estimation process, a convolutional-wavelield-based objective function for the preprocessed hydrophone component is used during acoustic FWI. By inverting the waveforms associated with early arrivals, a relatively high-resolution underground P-wave velocity model is obtained, with updates at a depth of 2.0 and 4.7 km. The initial S-wave velocity and density models are then constructed based on their prior relationships to the P-wave velocity, accompanied by a reciprocal source-independent elastic FWI to refine both velocity models. Compared with a traditional workflow, guided by stacking velocity analysis or migration velocity analysis, and using only the pressure component or other single component. the workflow presented in this study represents a good approach for inverting the 4C OBS data set to characterize subseafloor velocity structures.