Data Publication

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

Diab Montero, Hamed Ali | Stordal, Andreas Størksen | van Leeuwen, Peter Jan | Vossepoel, Femke

4TU.ResearchData

(2024)

Descriptions

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).

Keywords

Originally assigned keywords

Applied Mathematics

FOS: Mathematics

Geophysics

FOS: Earth and related environmental sciences

Earth Sciences

Mathematical Sciences

Data assimilation

Ensemble kalman filter

Particle flow filter

Adaptive Gaussian mixture filter

Lorenz 96

Deterministic chaos

Earthquake forecasting

Rate-and-state friction

Burridge-Knopoff

MSL enriched keywords

Measured property

friction - controlled slip rate

friction coefficient

stress relaxation

Inferred deformation behavior

deformation behaviour

frictional deformation

Measured property

friction - controlled slip rate

friction coefficient

tectonic deformation structure

tectonic fault

measured property

pH

MSL enriched sub domains i

rock and melt physics

analogue modelling of geologic processes

geochemistry

Source

https://data.4tu.nl/datasets/f0f075f2-f45c-4f8c-9d1d-bde03baeae33/1

Source publisher

4TU.ResearchData

DOI

10.4121/f0f075f2-f45c-4f8c-9d1d-bde03baeae33.v1

Creators

Diab Montero, Hamed Ali

https://orcid.org/:

0000-0003-2576-1116

Stordal, Andreas Størksen

van Leeuwen, Peter Jan

https://orcid.org/:

0000-0003-2325-5340

Vossepoel, Femke

https://orcid.org/:

0000-0002-3391-6651

Contributors

TU Delft, Faculty Of Civil Engineering And Geosciences, Department Of Geoscience And Engineering.

Other

University Of Bergen, Department Of Mathematics.

Other

Colorado State University, Department Of Atmospheric Science.

Other

University Of Reading, Department Of Meteorology.

Other

Citation

Diab Montero, H. A., Stordal, A. S., van Leeuwen, P. J., & Vossepoel, F. (2024). Data underlying the publication: Ensemble Kalman, Adaptive Gaussian Mixture, and Particle Flow Filters for Optimized Earthquake Forecasting (Version 1) [Data set]. 4TU.ResearchData. https://doi.org/10.4121/F0F075F2-F45C-4F8C-9D1D-BDE03BAEAE33.V1

Dates

Issued:

2024-04-12

Language

en

Funding References

Funder name: NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek)

Award number: Grant number: DEEP.NL.2018.037

Award title: InFocus: An Integrated Approach to Estimating Fault Slip Occurrence

Rights

Creative Commons Attribution 4.0 International

Datacite version

1