5:15 PM - 7:15 PM
[STT39-P04] New Time-Lapse Inversion Method Using Structural Coupling
Keywords:Time-lapse Inversion, Structural Coupling
In recent years, advancements in observation technologies, including UAV-based surveys, have facilitated repeated observations of electromagnetic data for structural monitoring of volcanoes and geothermal areas. These repeated observations have been utilized for time-lapse inversion of magnetization and resistivity structures (e.g., Minami et al., 2018; Bretaudeau et al., 2021). Traditional studies have mainly employed time-lapse inversion approaches such as parallel inversion, where model estimation is performed separately for each observation time. However, these approaches have been pointed out to have issues, including the susceptibility to apparent temporal changes and the unrobustness of differences in observation layouts (e.g., Kim et al., 2009; Calouris et al., 2011).
To address these issues, we propose a time-lapse inversion method that imposes structural coupling constraints in the time domain. Model calculations demonstrate that the proposed method is robust against changes in observation layout and effectively suppresses the detection of apparent temporal changes. Furthermore, we explore the feasibility of structural monitoring by integrating continuous observation data with repeated observation data. In this presentation, we will introduce these results and discuss the applicability of our approach to real-field observations.
To address these issues, we propose a time-lapse inversion method that imposes structural coupling constraints in the time domain. Model calculations demonstrate that the proposed method is robust against changes in observation layout and effectively suppresses the detection of apparent temporal changes. Furthermore, we explore the feasibility of structural monitoring by integrating continuous observation data with repeated observation data. In this presentation, we will introduce these results and discuss the applicability of our approach to real-field observations.