4:15 PM - 4:30 PM
[SEM16-10] Image focusing of conductive anomaly in CSEM data-space inversion using minimum support gradient regularization
Keywords:ACTIVE, controlled-source, inversion, volcano, monitoring
In this study, we developed a new 3-D inversion code for ACTIVE, by adopting an edge-based finite element method with unstructured tetrahedral mesh (e.g. Schwartzbach and Haber, 2013), data-space Gauss-Newton scheme (e.g. Kordy et al., 2016), and regularization by minimum support gradient (Xiang et al., 2017). Unstructured tetrahedral mesh allows us to change the spatial resolution of mesh efficiently in the vicinity of sources and receivers, and to express real topography accurately. The data-space Gauss-Newton scheme is reasonable for the case where the number of data is much smaller than that of model parameters, such as ACTIVE problem. Minimum support gradient regularization, presented by Xiang et al. (2017) originally for MT problems, can reduce the surface area of the obtained conductive anomaly and thereby focus the image of anomaly. We conducted synthetic inversion tests to confirm efficiency of our inversion method and to seek an efficient source-receiver configuration for ACTIVE observation, by prescribing a resistivity structure including a conduit-like conductive anomaly beneath the real active Nakadake 1st crater of Aso volcano as a true model. Our inversion method succeeded in delineating the conductive anomaly correctly with minimum support gradient regularization, especially for the top and side boundaries of the anomaly. Furthermore, we found that two or more sources dramatically improve inversion results as Mitsuhata et al. (2002) pointed out in their 2.5-D inversion implementations.
In the presentation, we introduce our new inversion method, and report the results of synthetic inversion tests and efficiency of minimum support gradient regularization in inversions for ACTIVE data.