EGU24-7990, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-7990
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of a Bayesian non-planar fault geometry inversion using geodetic seismic cycle deformation data

Guoguang Wei1,2, Kejie Chen1, and Luca Dal Zilio2
Guoguang Wei et al.
  • 1Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, China
  • 2Institute of Geophysics, Department of Earth Sciences, ETH Zürich, Zürich, Switzerland.

The geometry of faults regulates the spatial patterns of interseismic, coseismic, and post-seismic surface deformation. Geodetic techniques can measure these deformation patterns during a seismic cycle and are expected to constrain the geometry of  seismogenic faults. However, the conventional linear inversion of geodetic data is unable to simultaneously estimate the fault slip distribution and the fault geometry. In this study, we propose a Bayesian framework that treats fault geometry as a time-invariant parameter. It can individually use coseismic deformation data or simultaneously utilize interseismic, coseismic, and post-seismic deformation data to invert for both fault slip distribution and non-planar fault geometry. Within this framework, geometry evidence informed by geophysical imaging, geological surveys, and microseismicity forms the basis for establishing the prior probability density function, while geodetic observations constitute the likelihood function. Our methodology provides an ensemble of plausible geometry parameters by sampling the posterior probability distributions of the parameters using Markov Chain Monte Carlo simulation. The performance of the developed method is tested and demonstrated through inversions for synthetic oblique-slip faulting models. Results demonstrate that assuming constant rake can significantly bias fault geometry estimates and data weighting. Additionally, considering the variability of slip orientations allows for plausible estimates of non-planar fault geometry with objective data weighting.We applied the method to the 2013 Mw 6.5 Lushan earthquake in Sichuan province, China. The results reveal dominant thrust slips with left-lateral components and a curved fault geometry, with the confidence interval of the dip angles ranging between 20° and 25° and 56° and 58°. Furthermore, the application of this method to the 2015 Gorkha earthquake in Nepal sheds light on the Main Himalayan Thrust, which serves as the interface between the Indian Plate and Eurasia. This may provide new insights into future seismic potential and topographic growth in the Nepal Himalaya.

How to cite: Wei, G., Chen, K., and Dal Zilio, L.: Development of a Bayesian non-planar fault geometry inversion using geodetic seismic cycle deformation data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7990, https://doi.org/10.5194/egusphere-egu24-7990, 2024.