Joint Inversion of Seismic and DC Geophysical Data Using Local and Global Optimization Algorithms

Geophysical inversion is usually carried out to quantitatively analyze the earth model and estimate its physical properties. Successful delineation of these properties such as layer boundaries, or other near-surface structures are crucial to understand the near-surface inhomogeneity. In this study, we focus on the use of joint inversion of seismic refraction and geoelectrical resistivity datasets using local and global optimization methods. The idea is to integrate the two optimization techniques to minimize the challenges faced by each algorithm when applied alone. This hybrid algorithm (local and global) is applied on synthetic data representing simple resistivity and velocity models. About 70% of the anomalies in both seismic and DC resistivity methods were reconstructed in terms of amplitude and geometry using the local optimization algorithm, while the global optimization algorithm shows improved results as it reconstructed about 80% of the amplitude and geometry of the anomalies in both geophysical methods. The result of the synthetic application shows that the hybrid algorithm provides promising outputs in terms of resolution, geometry and amplitude of the anomalies, and computation run time.