Nonstationary spectral inversion algorithm based on reweighted TL1 norm
Seismic data reflectivity inversion is a critical step to connect reservoir parameters and seismic data,which remains a research hotspot.At present,reflectivity inversion is mostly in the form of the sparse-spike de-convolution based on the L1-norm constraint.In recent years,the emergence of the odd-even decomposition al-gorithm has weakened the inter-wavelet tuning effect,which makes the spectral inversion based on the L1-norm constraint receive further applications.The sparse constraint ability is related to the accuracy of the inversion re-flectivity remaining to be solved.Because of the insufficient sparsity constraint ability of the L1-norm and the Lp norm,this paper introduce the transformed L,(TL1)sparse constraint,which is conducive to the obtainment of a more accurate inversion reflectivity.Meanwhile,given that the fitting ability of the large reflectivity needs to be enhanced,this paper propose the reweighted TL1(RTL1)norm to further enhance the sparse constraint abi-lity.Parameter tests show that the reconstruction ability of the reweighted norm is better than that of the non-re-weighted norm,which proves the effectiveness of the RTL,norm in sparse reconstruction.Models and field data processing demonstrate that the RTL,norm is more effective in enhancing the accuracy of reflectivity inver-sion in spectral inversion compared to conventional sparse constraints.