A way to synchronize models with seismic faults for earthquake forecasting: Insights from a simple stochastic model
Alvaro Gonzalez, Miguel Vazquez-Prada, Javier B. Gomez, Amalio F. Pacheco
Abstract
Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models. The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized with the first one. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first model. This forecasting strategy outperforms others that only take into account the earthquake series. Our results suggest that probably a good way to synchronize more detailed models with real faults is to force them to reproduce the sequence of previous earthquake ruptures on the faults. This hypothesis could be tested in the future with more detailed models and actual seismic data.
Create a lesson
Related papers
DiffSpecMatch: Differentiable Waveform Inverse Design with Configurable Optimization Modules for Response-Spectrum Matching of Ground Motions
Xiaohu Hu, Su Chen, Zhijun Dai et al.
Physics-based deep learning for non-stationary adaptive multiple suppression
Chao Li, Yangkang Chen
A Multi-frame Analysis of Flow Reversal in Tidal Bore
Assia Belemkadem, Johan Fourdrinoy, Anthony Beaudoin et al.
Event-Centric Earthquake Monitoring with a Multi-Station Transformer
Haoyu Wang, Weiqiang Zhu
Bayesian Inversion Beyond the Generator's Range: Reducing Non-physical Constraints from Generative Neural Networks
Pratyay Roy, Xuebin Zhao, Andrew Curtis
Global Structure-Constrained 4D prestack inversion
Qingwei Zhao, Xuehua Chen, Shengyi Wang et al.