Joint elastic reverse time migration of towed streamer and sparse ocean-bottom seismic node hybrid data

Abstract

Compared with one-component towed-streamer (TS) seismic exploration, four-component ocean bottom seismic (OBS) node exploration has great advantages in complex structure imaging, lithology and fluid identification using elastic waves. However, sparse spatial sampling of OBS node surveys highlights the problems of the imaging acquisition footprint, poor phase continuity, and low signal-to-noise (S/N) ratio with conventional elastic reverse-time migration (ERTM) methods. Therefore, a solution of joint ERTM (J-ERTM) is proposed by combining sparse OBS node data with dense TS data. In the J-ERTM of the hybrid data, a novel weighted boundary condition combined with acoustic-elastic coupling equations and a vector-based cross-correlation imaging condition are presented to perform PP and PS imaging by receiver-side tensorial extrapolation of TS and OBS node hybrid data. A synthetic example demonstrates that the proposed method can effectively process TS and OBS node hybrid data and improve elastic imaging problems caused by OBS node sparse acquisition. J-ERTM techniques are also applied to an active-source ocean-bottom seismic dataset from the South China Sea. To improve the imaging quality with limited data, some pre-processing procedures for field TS and OBS node hybrid datasets are necessary, such as denoising, OBS node relocation, OBS node orientation correction, OBS node calibration, et al. After pre-processing, the pressure component and velocity components have a more physical energy relationship, more consistent frequency range and wavelet, and a higher S/N ratio. Finally, the pre-processed hybrid data can be used for J-ERTM. The imaging results show that the proposed method works well with field data.