Low-Dimensional Multi-Trace Impedance Inversion in Sparse Space with Elastic Half Norm Constraint

Recently, multi-trace impedance inversion has attracted great interest in seismic exploration because it improves the horizontal continuity and fidelity of results by exploiting lateral structure information strata. However, computational inefficiency affects its practical application. Furthermore, terms vertical constraints on model parameters, only considers smooth features while ignoring sharp discontinuity features. This leads to yielding an over-smooth solution that does not accurately reflect distribution underground rock. To deal with above situations, we first develop a low-dimensional (LMII) framework. Inspired compressed sensing, this framework utilizes measurements sparse space containing maximum signal construct objective function for inversion, which can significantly reduce size problem improve inverse efficiency. Then, introduce elastic half (EH) norm as constraint parameters LMII formulate novel constrained inversion. Because introduced EH takes into account both smoothness blockiness rock impedance, effectively raise accuracy complex Finally, efficient alternating multiplier iteration algorithm is derived based variable splitting technique optimize model. The performance developed approaches tested using synthetic data, prove their feasibility superiority.