Joint PP and PS Pre-stack Seismic Inversion for Stratified Models Based on the Propagator Matrix Forward Engine

• Published in: Surveys in geophysics 2020-09, Vol.41 (5), p.987-1028
• Main Authors: Luo, Cong, Ba, Jing, Carcione, José M., Huang, Guangtan, Guo, Qiang
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Astronomy; Bulk density; Computer applications; Conditional probability; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Inversions; Lithology; Mathematical models; Model testing; Modelling; Noise levels; Observations and Techniques; Optimization; P waves; Parameter sensitivity; Probability theory; Ray tracing; Reflectance; Regularization; S waves; Seismic exploration; Seismic stability; Seismic surveys; Seismic velocities; Wave velocity; Windows (intervals)
• ISSN: 0169-3298; 1573-0956
• DOI: 10.1007/s10712-020-09605-5

Pre-stack seismic inversion of the P- and S-wave velocities and bulk density is important in seismic exploration for evaluating lithological units and fluid properties. Generally, this inversion is based on ray-tracing modeling, which introduces errors and requires substantial pre-processing for stratified models due to its oversimplified single-interface assumption. To overcome those problems, we propose a pre-stack inversion method, using wave-equation-based modeling as a forward engine. Most wave-equation-based pre-stack inversions are based on the reflectivity method and adopt nonlinear optimization algorithms, although accurate, but computationally expensive. Hence, we use a fast propagator matrix (PM) method valid for layered media. To improve the stability and accuracy, the PP data inversion is extended to joint PP and PS PM-based inversion (JPMI). A linear inversion scheme is adopted to reduce the computational cost, and the Fréchet derivatives are computed accordingly. Moreover, to obtain an optimal model solution, the L-BFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) optimization algorithm and L-curve criterion, an adaptive regularization parameter acquisition method, are implemented. A posterior probability analysis shows that the method has a higher parameter sensitivity than the joint exact Zoeppritz-based inversion and gives better estimations than the single-data inversion. We discuss the effects of dataset weight, internal multi-reflections, time window setting, noise level and initial model by using model tests. Synthetic and real-data examples demonstrate that the algorithm is better than the single PP inversion in terms of stability and accuracy, especially for S-wave velocity and density estimations.