On parameter bias in earthquake sequence models using data assimilation
<p>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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-04-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | https://npg.copernicus.org/articles/30/101/2023/npg-30-101-2023.pdf |
| Summary: | <p>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 zero-dimensional (0D) generalization of the Burridge–Knopoff spring–block model with rate-and-state
friction. Minor changes in the friction parameter <span class="inline-formula"><i>ϵ</i></span> can lead to different state trajectories and earthquake characteristics. The
performance of data assimilation with respect to estimating the fault state in the presence of a parameter bias in <span class="inline-formula"><i>ϵ</i></span> depends on the magnitude of the
bias. A small parameter bias in <span class="inline-formula"><i>ϵ</i></span> (<span class="inline-formula">+3</span> %) can be compensated for very well using state estimation (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.99), whereas an
intermediate bias (<span class="inline-formula">−</span>14 %) can only be partly compensated for using state estimation (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.47). When increasing particle spread by accounting for model error and
an additional resampling step, <span class="inline-formula"><i>R</i><sup>2</sup></span> increases to 0.61. However, when there is a large bias (<span class="inline-formula">−</span>43 %) in <span class="inline-formula"><i>ϵ</i></span>, only state-parameter
estimation can fully account for the parameter bias (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.97). Thus, simultaneous state and parameter estimation effectively separates the
error contributions from friction and shear stress to correctly estimate the current and future shear stress and slip rate. This illustrates the
potential of data assimilation for the estimation of earthquake sequences and provides insight into its application in other nonlinear processes with
uncertain parameters.</p> |
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| ISSN: | 1023-5809 1607-7946 |