Seismic Response Prediction and Velocity Model Building Inversion by the Whale Optimization Algorithm

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm that is based on the bubble-net hunting strategy used by humpback whales. The objective of this study is to assess the utilization of the WOA to perform the inversion of seismic data. The primary focus of this optimization is to first target an objective function for inversion that leads to minimizing the RMS error between field observed data and whale predicted data. The algorithm maintains a balance between exploration and exploitation phases that results in a better solution to the desired problem. The application of this technique to 12 benchmark models and validation through a true Vp model helps to reconstruct P-wave velocity and true model parameter estimation. The performance of the algorithm is compared with the gray wolf optimization (GWO) algorithm. The experimental results show that the final model after a reasonable number of iterations is able to provide optimum solutions within 10 different randomly generated search populations. As a consequence, it can be stated that the approach has higher reliability to reveal the P-wave imaging with a good convergence rate and minimum uncertainty over any complex geology.