Multi-Level Genetic Algorithm (GA)-based Acoustic-Elastodynamic Imaging of Coupled Fluid-Solid Media to Detect an Underground Cavity

Abstract

This work studies the feasibility of imaging a coupled fluid-solid system by using the elastodynamic and acoustic waves initiated from the top surface of a computational domain. We consider a one- dimensional system, where a fluid layer is surrounded by two solid layers. The bottom solid layer is truncated by using a wave-absorbing boundary condition (WABC). We measure the wave responses on a sensor located on the top surface, and the measured signal contains information about the underlying physical system. By using the measured wave responses, we identify the elastic moduli of the solid layers and the depths of the interfaces between the solid and fluid layers. We employ a multi-level Genetic Algorithm (GA) combined with a frequency-continuation scheme to invert for the values of sought-for parameters. The numerical results show that the following findings. First, the depths of solid-fluid interfaces and elastic moduli can be reconstructed by the presented method. Second, the frequency-continuation scheme improves the convergence of the estimated values of parameters toward their targeted values. Lastly, the minimizer using a frequency-continuation system that increases the signal’s dominant frequency in each GA level is as effective as the other that decreases the signal’s dominant frequency. If this work is extended to a 3D setting, it can be instrumental to finding unknown locations of fluid-filled voids in geological formations that can lead to ground instability and/or collapse (e.g., natural/anthropogenic sinkhole, urban cave-in subsidence, etc.).