Direct estimation of the fault slip of short-term slow slip events from GNSS data using deep learning

In this study, we aim to develop a deep-learning method to monitor spatio-temporal evolutions of short-term SSEs based on a dense GNSS network. We theoretically create two types of noiseless three-dimensional (3D) deformation data assuming 272 subfaults in western Shikoku, southwest Japan; 16 subfaults along the strike multiplied by 17 subfaults along the dip. One is deformations at 95 GNSS stations, and the second is those at 900 virtual stations which are regularly located over the target area. We tailor two supervised-learning models to estimate the slip area and the slip amount of SSE by learning those deformation images as input data. Nakagawa et al. (2021, Fall Meeting in Geodetic Society of Japan) showed that the model trained with GNSS stations estimated SSEs with 91.8% variance reduction (VR) while the other model achieved 98.3% VR. We concluded that this difference in estimation accuracy is contributed to the dissimilarity between input deformation images. Therefore, we newly implement Model-supervised Interpolation (MSI) approach to overcome this problem. MSI successfully reproduces the deformations at 900 virtual stations only from the deformations at 95 GNSS stations with 97.4% VR although nearly half of the target area is located on the offshore region. Additionally, a model trained with deformation images predicted by MSI improves the VR of the slip estimation by 0.5%.
The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)