Joint Reflectivity and Structural Interval-Q Estimation by Using Nonstationary Sparse Inversion

Reliable estimate of the anelastic attenuation factor- Q from seismic records is highly desirable for improving seismic resolution. However, the conventional equivalent- Q or horizontal interval- Q estimation ignores that Q-distribution should hold the same ability for the subsurface structure characterization as seismic data. To pursue an accurate Q-model, we propose a technique for joint reflectivity and structural interval- Q estimation by using nonstationary sparse inversion. We designed a structural interval- Q model by dividing the seismic data into several structural layers with the interpreted horizon(s). Attenuations in each layer are close to each other and can be described by an equivalent- Q or gradient- Q. Based on the attenuation theory, the nonstationary sparse inversion is solved iteratively, where, at each iteration, the equivalent- Q of only one layer is optimized by searching for the corresponding optimum inverted reflectivity, leading to a structural interval- Q model. The main advantages of our method are its objectivity and accuracy because of the integration of the prior structural information from interpreted horizons into joint reflectivity-estimation and Q-estimation. The test of synthetic and field data clearly illustrates that the proposed method enables high-precision structural interval- Q estimation and sufficiently compensates for Q-related attenuation.