Multilevel Genetic Algorithm–Based Acoustic–Elastodynamic Imaging of Coupled Fluid–Solid Media to Detect an Underground Cavity

Abstract

This work studied the feasibility of imaging a coupled fluid–solid system using the elastodynamic and acoustic waves initiated from the top surface of a computational domain. A one-dimensional system, in which a fluid layer was surrounded by two solid layers, was considered. The bottom solid layer was truncated using a wave-absorbing boundary condition (WABC). The wave responses were measured by a sensor located on the top surface, and the measured signal contained information about the underlying physical system. Using the measured wave responses, the elastic moduli of the solid layers and the depths of the interfaces between the solid and fluid layers were identified. A multilevel genetic algorithm (GA) combined with a frequency-continuation scheme to invert the values of sought-for parameters was employed. The numerical results showed that (1) the depths of solid–fluid interfaces and elastic moduli can be reconstructed by the presented method, (2) the frequency-continuation scheme improves the convergence of the estimated values of parameters toward their targeted values, and (3) a preliminary inversion using an all-solid model can be employed to identify if a fluid layer exists in the model by showing one layer with a very large value of Young’s modulus (with a value similar to that of the bulk modulus of water) and a mass density similar to that of water. Then the primary GA inversion method, based on a fluid–solid model, can be utilized to adjust the soil characteristics and fine-tune the locations of the fluid layer. If this work is extended to a 3D setting, it can be instrumental in finding unknown locations of fluid–filled voids in geological formations that can lead to ground instability and/or collapse (e.g., natural or anthropogenic sinkholes, urban cave-in subsidence, and so forth).