Sensitivity analysis of current continuous GPS networks to monitor volcanic unrest

• Main Author: Lee, Daniel Wei Jie
• Other Authors: Benoit Taisne
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Science::Geology::Volcanoes and earthquakes
• Online Access: https://hdl.handle.net/10356/165742
• Institution: Nanyang Technological University

Real-time monitoring of volcanic ground deformation is a compelling tool for hazard management and disaster response during volcanic crises. However, monitoring in real-time is stymied by the credibility of source models and the detection capability of the current continuous Global Positioning System (cGPS) network. Many post-eruptive studies use inversion on various source models on the same ground deformation data, but arrived at substantially different forecasted source parameters. To highlight these differences, we use a Bayesian inversion with a simulated annealing method on static synthetic deformation data. We show the extent of errors with assuming a fixed geometry, in the cases of the commonly-used point source and rectangular dislocation models. We also demonstrate how a more complex point compound dislocation model can be used to evaluate unknown deformation sources, with the potential for real-time application. Subsequently, we model synthetic deformation on cGPS networks on 184 volcanoes, with station information retrieved from the World Organization of Volcano Observatory, Global Volcano Monitoring Infrastructure Database (WOVOdat-GVMID), to establish a preliminary assessment of the capability of current cGPS networks. We find that various factors, such as the number of varied model parameters, the type of deformation and the station positioning, significantly affect the success of the inversion modeling. We also specifically analyze four well-equipped networks, namely Izu-Oshima, Miyake-jima, San Cristobal and Sierra Negra, to emphasize the limitations and benefits of various network designs. Developing a comprehensive real-time monitoring network will require additional extensive modeling that includes known geological processes and subsurface structures to test the fidelity of a network.