Preprints
https://doi.org/10.5194/egusphere-2022-766
https://doi.org/10.5194/egusphere-2022-766
 
07 Sep 2022
07 Sep 2022

On Parameter Bias in Earthquake Sequence Models using Data Assimilation

Arundhuti Banerjee1, Ylona van Dinther2, and Femke C. Vossepoel1 Arundhuti Banerjee et al.
  • 1Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
  • 2Department of Earth Sciences, Utrecht University, Princetonlaan 4,3584 CB Utrecht, the Netherlands

Abstract. The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. We investigate whether data assimilation can estimate current and future fault states, i.e., slip rate and shear stress, in the presence of a bias in the friction parameter. We perform state estimation as well as combined state-parameter estimation using a sequential importance resampling particle filter in a 0D generalization of the Burridge–Knopoff spring-block model with rate-and-state friction. Minor changes in the friction parameter epsilon can lead to different state trajectories and earthquake characteristics. The performance of data assimilation in estimating the fault state in the presence of a parameter bias in epsilon depends on the magnitude of the bias. A small parameter bias in epsilon (+3 %) can be compensated very well using state estimation (R2= 0.99), whereas an intermediate bias (-14 %) can only be compensated partly (R2= 0.47). When increasing particle spread by accounting for model error and an additional resampling step R2 increases to 0.61. However, when there is a large bias (-43 %) in epsilon, only state-parameter estimation can fully account for the parameter bias (R2= 0.97). Simultaneous state- and parameter estimation thus effectively separates error contributions from friction and shear stress to correctly estimate current and future shear stress and slip rate. This illustrates the potential of data assimilation for estimation of earthquake sequences and provides insight into its application in other non-linear processes with uncertain parameters.

Arundhuti Banerjee et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-766', Anonymous Referee #1, 15 Sep 2022
    • AC1: 'Reply on RC1', Arundhuti Banerjee, 05 Jan 2023
  • RC2: 'Comment on egusphere-2022-766', Anonymous Referee #2, 02 Oct 2022
    • AC2: 'Reply on RC2', Arundhuti Banerjee, 05 Jan 2023
    • AC3: 'Reply on RC2', Arundhuti Banerjee, 05 Jan 2023

Arundhuti Banerjee et al.

Arundhuti Banerjee et al.

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Short summary
The feasibility of physics-based forecasting of earthquakes depends on how well models can be calibrated to represent earthquake scenarios given uncertainties in both models and data. Our study investigates whether data assimilation can estimate current and future fault states in the presence of a bias in the friction parameter.