Spatially Correlated Reflectivity Reconstruction via a Two-Step Scheme

Abstract

Summary Sparse spike inversion (SSI), imposes a sparseness constraint term along seismic trace, can evidently broaden the effective band of seismic data. However, this method frequently suffers from instability and poor continuity issues due to neglecting of the spatial dependence among reflectivity at adjacent traces. Although some methods add a lateral constraint item into cost function to consider above spatial correlations, the complicate coupling effect between the triggered two trade-off parameters severely limits the algorithm’s performance. We develop a two-step multichannel reflectivity inversion algorithm (TS-MRI) to retrieve spatially correlated reflectivity while avoiding opting for the two weights simultaneously. In the first step, we apply SSI to fast obtain sparse reflectivity estimation. In the second step, we exploit the result from SSI, a data-driven structural constraint term, and a least-square framework to reconstruct multi-trace reflectivity. The reflection structure characteristics (RSC) estimation plays a key role in building the structural constraint term, which has ability to map the spatial geometrical association in data into inverted reflectivity image. A model and a field data examples confirm the merits of TS-MRI than SSI on guaranteeing the continuity of structures and protecting weak events.