Data underlying the publication: Ensemble Kalman, Adaptive Gaussian Mixture, and Particle Flow Filters for Optimized Earthquake Forecasting

doi:10.4121/f0f075f2-f45c-4f8c-9d1d-bde03baeae33.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/f0f075f2-f45c-4f8c-9d1d-bde03baeae33
Datacite citation style:
Diab Montero, Hamed Ali; Stordal, Andreas Størksen ; van Leeuwen, Peter Jan ; Vossepoel, Femke (2024): Data underlying the publication: Ensemble Kalman, Adaptive Gaussian Mixture, and Particle Flow Filters for Optimized Earthquake Forecasting. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/f0f075f2-f45c-4f8c-9d1d-bde03baeae33.v1
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Dataset

Time series from a Lorenz 96 model and a Burridge-Knopoff model coupled with rate-and-state friction using the non-dimensional formulation of Erickson et al. 2011 (https://academic.oup.com/gji/article/187/1/178/560601). The time series of the 1-D Burridge-Knopoff model of 20 blocks includes the evolution of the shear stress, velocity, slip, and state theta. The time series of the Lorenz 96 model with 20 cells includes the evolution of the state x. The time series were used for the sensitivity analysis of the changes in the recurrence intervals for different values of the parameter epsilon (sensitivity of the velocity relaxation) in Chapter 2 (Numerical modeling of earthquakes), the perfect model experiments in Chapter 3 (Ensemble data assimilation methods), and the perfect model experiments on Chapter 5 (Non-Gaussian ensemble data assimilation methods for optimized earthquake forecasting) of the Ph.D. thesis "Ensemble data assimilation methods for estimating fault slip and future earthquake occurrences", and for the publication "Ensemble Kalman, Adaptive Gaussian Mixture, and Particle Flow Filters for Optimized Earthquake Forecasting" prepared for submission. The estimates of the perfect model experiment correspond to three different ensemble data assimilation methods, namely the Ensemble Kalman Filter (EnKF), the Adaptive Gaussian Mixture Filter (AGMF), and the Particle Flow Filter (PFF).

history
  • 2024-04-12 first online, published, posted
publisher
4TU.ResearchData
format
txt files
funding
  • InFocus: An Integrated Approach to Estimating Fault Slip Occurrence (grant code Grant number: DEEP.NL.2018.037) [more info...] NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek)
organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Engineering.
University of Bergen, Department of Mathematics.
Colorado State University, Department of Atmospheric Science.
University of Reading, Department of Meteorology.

DATA

files (1)