Automated Picking of Time-Velocity Pairs Using a Hybrid Grey Wolf Optimizer

Reflection seismic is crucial for geophysical exploration, analyzing Earth's subsurface layers through reflected waves. The precision of Normal MoveOut (NMO) correction greatly influences effectiveness, requiring careful selection of time-velocity pairs. Manual selection is challenging due to diverse, extensive field data. Our study automates this process using the Semblance coherence panel, identifying coherent patterns to optimize pair picking. This approach has yielded promising results. The Hybrid Grey Wolf Optimizer (HGWO), incorporating genetic operators and Simulated Annealing (SA) algorithms, was employed for automated pair picking. Wilcoxon tests confirmed their equivalence and acceptable solutions, showing correct minimization of the objective function and algorithmic convergence to accurate pairs. Automation simplifies the process, enhancing the quality and accuracy of geological interpretations, refining analysis of reflection seismic data. Integrating Semblance into automated picking represents a significant advancement, streamlining procedures and improving reliability. This is vital for accurate decision-making in exploratory studies and geological mapping.