Data-driven acoustic impedance inversion with reweighted L1 norm sparsity constraint

Acoustic impedance (AI) inversion is widely used in geophysics and reservoir prediction. But the traditional method cannot fully exploit sparse characteristics of geological attributes. There are problems with multiplicity low resolution. To solve this problem, a data-driven acoustic reweighted L1 norm constraints (DRL1) proposed. In process, local cross-correlation analysis introduced to above problems. The as constraint (RL1) replace which constrained by norm. RL1 can describe more sparsity information improve resolution inversion. addition, quality seismic data plays decisive role We add process. evaluated rationality each sampling point introducing analysis, controlling for their contribution inversion, making results stable accurate. objective function solved alternating direction multiplier (ADMM) algorithm soft threshold shrinkage algorithm. Finally, we validate effectiveness proposed through model tests field data. show that our not only provides accurate portrayal stratigraphy, but also yields results.