Robust spectral inversion based on the stability factor

Reflectivity inversion is a critical step to joint reservoir parameters and seismic data. The sparse spike deconvolution is a widely used reflectivity inversion method based on the L₁ norm constraint. But the wavelet effect limits the resolution of the algorithm. The emergence of the odd–even decomposition algorithm has weakened the wavelet tuning effect, which makes the spectral inversion based on the L₁ norm further applied. Because of the instability of the spectral inversion algorithm, the lateral continuity of the inversion reflectivity is poor. Therefore, based on the conventional spectral inversion, we introduced a stability factor and proposed a robust spectral inversion method. The algorithm inherits the high-resolution characteristics of conventional spectral inversion and the robustness of sparse spiking deconvolution. The performances of three reflectivity inversion methods from synthetic and field data examples demonstrate the improvements in resolution and stability of the robust spectral inversion algorithm.