Full-parametric and joint inversion of multimode surface wave data for identifying glacial ice thickness and freezing extent in subglacial sediments via the hunger games search algorithm

Abstract

Determining glacier ice thickness and the extent of freezing in subglacial sediments are crucial in glaciological studies. Noninvasive geophysical methods, such as multichannel analysis of surface waves, are typically used for these tasks. In this study, we introduce a novel metaheuristic called hunger games search (HGS), which simulates the hunger-driven instincts and behavioral decisions of animals for the full-parametric inversion of seismic surface waves. We apply HGS to determine layer thicknesses, densities, S-wave velocities, and primary wave velocities of different layers through the joint inversion of multimode Rayleigh wave dispersion curves (RWDCs). This marks the first study using HGS for the inversion of dispersion data. Sensitivity studies of model parameters prior to the inversion indicate the necessity of postinversion uncertainty evaluations to mitigate the effects of varying sensitivity levels. In addition, a parameter tuning study is carried out to maximize the performance of HGS. Compared with some swarm intelligence-based optimizers (particle swarm optimization, cuckoo search, gray wolf optimization, sparrow search optimization, and whale optimization algorithm), HGS outperforms in the inversion of synthetic multimode RWDCs with 10% uncertainties with respect to a simulated glacier structure. In real data applications, a data set acquired at Midtdalsbreen, an outlet of the Norwegian Hardangerj & oslash;kulen ice cap, is inverted using the tailored HGS metaheuristic. The results obtained are consistent with previous geophysical studies. Furthermore, our analysis reveals that the performance of HGS when dealing with real applications is not highly sensitive to the selection of layers within the range of five to eight. The accuracy of HGS is undoubtedly contaminated by a larger model space; however, additional depth information derived from colocated ground-penetrating radar data can be directly integrated into HGS to obtain satisfactory results again.