Advancing post-stack seismic inversion through music-inspired harmony search optimization technique. A case study - Kant, Ravi; Kumar, Brijesh; Maurya, S.P.; Narayan, Satya; Singh, A.P.; Hema, G. (Geoenergy Science and Engineering, 2025)
A novel post-stack seismic inversion algorithm has been developed to estimate acoustic impedances using P-wave reflection seismic data, employing the music-inspired harmony search global optimization (HSO) technique. This optimization seeks to find the global minimum of the objective function, which measures the misfit between synthetic and observed post-stack seismic data. During the iterative inversion process, acoustic impedance models are randomly perturbed, and synthetic seismic data are recalculated to match observed data. To enhance stability, the algorithm uses constraints from a well-log-derived low-frequency impedance model. The proposed algorithm was tested on synthetic and real data to demonstrate its effectiveness in post-stack seismic data inversion. On synthetic test, we found high accuracy of the HSO-generated traces, with average correlations of 0.99, 0.99, 0.97, and 0.96, and RMS errors of 0.12, 0.40, 0.50, and 0.62, for noise levels of 0 %, 10 %, 20 %, and 30 %, respectively. For real data from the Blackfoot Field, Alberta, Canada, the algorithm achieved a 0.93 correlation and 0.22 RMS error, enabling seismic data inversion for acoustic impedance estimation. The inverted section identified low acoustic impedance (8000–9000 m/s∗g/cc), matching the high seismic amplitude anomaly, suggesting a sand channel reservoir between 1040 and 1065 ms two-way travel time. While, high acoustic impedance (9000–12000 m/s∗g/cc) indicating background shale facies. This study explores potential hydrocarbon reservoirs in the Blackfoot Field, Alberta, using HSO-based advanced global optimization for efficient and accurate seismic data inversion. •Developed a novel post-stack seismic inversion algorithm using harmony search optimization to estimate acoustic impedances.•HSO techniques exhibited outstanding performance across various Gaussian noise levels, reaching up to 30 %.•HSO's algorithm performance remains consistent regardless of the increasing number of sample points.•HSO achieved faster convergence with fewer iterations, accurately identifying a sand channel in Blackfoot field.•Compared with model-based inversion, HSO provides clearer, continuous impedance boundaries and resolves thin layers better.