$Q$ Estimation by Combining ISD With LSR Method Based on Shaping-Regularized Inversion

Abstract

The quality factor $Q$ is an indispensable parameter for studying wave propagation in viscoelastic media. $Q$ can not only be implemented for improving the quality of wave records but can also be used for directly indicating frequency-dependent anomalies induced by fluids, so it is widely used in seismic exploration and clinical medicine. $Q$ estimation here refers to the extraction of $Q$ information from seismic data; it has aroused lots of attention but is still somewhat controversial due to the limitations of existing methods. In this letter, combined with a logarithmic spectral ratio (LSR) algorithm, we have introduced a sparse-constrained inversion spectral decomposition (ISD) method for average- $Q$ estimation (LSR-ISD), and have used shaping regularization to solve for the spectrum ratio. Then, through regularized linear inversion, average- $Q$ was converted to an interval- $Q$ value. Finally, we have applied this method to synthetic data and field data. Numerical examples and field data application demonstrate that the proposed method produces a series of results with high resolution and good stability.