Published February 2024 | Published
Journal Article Open

An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon The University of Texas at Austin

Abstract

Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson's ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth's structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.

Copyright and License

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Acknowledgement

SP, TWB and DL were supported by NSF grants EAR-2121666, EAR-2045292, EAR-19214743 and EAR-1927216. UV and OG were supported by NSF grant ACI-1550593 and DOE grant ASCR DE-SC0019303. We thank Jeffrey Freymueller and Charles Williams for their helpful comments and suggestions to improve the quality of the manuscript.

Funding

SP, TWB and DL were supported by NSF grants EAR-2121666, EAR-2045292, EAR-19214743 and EAR-1927216. UV and OG were supported by NSF grant ACI-1550593 and DOE grant ASCR DE-SC0019303.

Data Availability

The Jupyter notebooks and codes for reproducing the results are available in the online GitHub repository at https://github.com/SimonePuel/PoissonRatio-Joint-Inversions.git. We utilized FEniCS-2019.1.0 and hIPPYlib-3.0.0 to compute all the results in this study. These libraries can be downloaded at https://fenicsproject.org and https://hippylib.github.io, respectively. The unstructured meshes for the FE computations were generated using the open-source software Gmsh (Geuzaine & Remacle 2009), and the corresponding files are included in the online repository.

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Additional details

Created:
January 24, 2025
Modified:
January 24, 2025