Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses

Dinther, Ylona van; Künsch, Hans R.; Fichtner, Andreas

doi:10.1093/gji/ggz063

Cited by 49 publications

(22 citation statements)

References 102 publications

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“…Gao and Wang (2017) showed that their model reconciles a wide range of observations and proposed it as a new concept for the subduction seismicity. van Dinther et al (2013avan Dinther et al ( , 2013bvan Dinther et al ( , 2014van Dinther et al ( , 2019avan Dinther et al ( , 2019b developed, validated, and applied the first continuum-based, seismo-thermo-mechanical (STM) subduction model based on visco-elasto-plastic geodynamic code I2ELVIS (Gerya and Yuen, 2007). The STM approach was validated with laboratory experiments (Corbi et al, 2013) and includes velocity-weakening friction to spontaneously generate a series of fast frictional instabilities that correspond to earthquakes.…”

Section: ■ Subduction-induced Seismicitymentioning

confidence: 99%

“…van Dinther et al (2019a) explained the uplift by superposition of two physical mechanisms: (1) elastic buckling of a visco-elastically layered forearc that is horizontally compressed in the interseismic period and (2) mass conservation-driven return flow following accelerated slab penetration due to the megathrust earthquake. Recently, van Dinther et al (2019b) also showed that the significant predictive power of STM subduction models can potentially help seismic hazard assessment through probabilistic estimation and forecasting of megathrust stresses based on ensemble data assimilation for earthquake sequences. Herrendörfer et al (2015) used simplified seismo-mechanical subduction models to investigate physical controls for supercycle behavior of seismicity: a long-term cluster of different sizes of megathrust earthquakes, leading up to the final complete failure of a subduction zone segment (e.g., Sieh et al, 2008;Goldfinger et al, 2013).…”

Section: ■ Subduction-induced Seismicitymentioning

confidence: 99%

“…In the future, numerical modeling of subduction-induced seismicity will likely grow in scope because this research direction shows significant scientific, methodological, and applied potential (e.g., Herrendörfer et al, 2015;Gao and Wang, 2017;Dal Zilio et al, 2018a, 2019van Dinther et al, 2019b). Coupling of geodynamic evolution and seismicity through various hybrid existing and newly developed seismo-(hydro)-(thermo)-mechanical approaches (e.g., van Dinther et al, 2013avan Dinther et al, , 2013bSobolev and Muldashev, 2017;Herrendörfer at al., 2018;Petrini et al, 2020) will likely remain as the prominent trend.…”

Section: ■ Subduction-induced Seismicitymentioning

confidence: 99%

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Numerical modeling of subduction: State of the art and future directions

Gerya

2022

Geosphere

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During the past five decades, numerical modeling of subduction, one of the most challenging and captivating geodynamic processes, remained in the core of geodynamic research. Remarkable progress has been made in terms of both in-depth understanding of different aspects of subduction dynamics and deciphering the diverse and ever-growing array of subduction zone observations. However, numerous key questions concerning subduction remain unanswered defining the frontier of modern Earth Science research. This review of the past decade comprises numerical modeling studies focused on 12 key open topics: • Subduction initiation • Subduction termination • Slab deformation, dynamics, and evolution in the mantle • 4D dynamics of subduction zones • Thermal regimes and pressure-temperature (P-T) paths of subducted rocks • Fluid and melt processes in subduction zones • Geochemical transport, magmatism, and crustal growth • Topography and landscape evolution • Subduction-induced seismicity • Precambrian subduction and plate tectonics • Extra-terrestrial subduction • Influence of plate tectonics for life evolution. Future progress will require conceptual and technical progress in subduction modeling as well as crucial inputs from other disciplines (rheology, phase petrology, seismic tomography, geochemistry, numerical theory, geomorphology, ecology, planetology, astronomy, etc.). As in the past, the multi-physics character of subduction-related processes ensures that numerical modeling will remain one of the key quantitative tools for integration of natural observations, developing and testing new hypotheses, and developing an in-depth understanding of subduction. The review concludes with summarizing key results and outlining 12 future directions in subduction and plate tectonics modeling that will target unresolved issues discussed in the review.

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Section: ■ Subduction-induced Seismicitymentioning

confidence: 99%

Section: ■ Subduction-induced Seismicitymentioning

confidence: 99%

Section: ■ Subduction-induced Seismicitymentioning

confidence: 99%

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Numerical modeling of subduction: State of the art and future directions

Gerya

2022

Geosphere

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“…This is partly because the adjoint method linearizes the nonlinear forward simulation. To avoid such an effect, it is possible to develop a hybrid data assimilation approach by utilizing an ensemble-based approach, such as EnKF (Hirahara and Nishikiori 2019;van Dinther et al 2019) or the simple grid calculation (Kano et al 2015), which do not require the linearization of the forward equation, for example, to obtain better first-guess values then optimize them using the adjoint method. Another disadvantage of the adjoint method is that it is difficult to directly obtain the uncertainties of the optimized model parameters.…”

Section: Future Improvementsmentioning

confidence: 99%

Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

Kano

Miyazaki

Ishikawa

et al. 2020

Earth Planets Space

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Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

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“…To estimate and predict the fault state such as slip velocities and stress states, van Dinther et al (2019) and Hirahara and Nishikiori (2019) have introduced the ensemble Kalman filter (EnKF), one of the sequential data assimilation methods. The EnKF sequentially updates the simulation variables and/or physical parameters of interest every time the observations get acquired.…”

Section: Introductionmentioning

confidence: 99%

Adjoint-based Direct Data Assimilation of Gnss Time Series for Optimizing Frictional Parameters and Predicting Postseismic Deformation Following the 2003 Tokachi-oki Earthquake

Kano

Miyazaki

Ishikawa

et al. 2020

Preprint

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Abstract PostseismicGlobal Navigation Satellite System (GNSS) time seriesfollowed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip andcan be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important toquantitativelycapture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developedanadjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault.The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip can be roughly reproduced but not in detail if the observation noise is included. The optimization of frictional parameters provides not only the reproduction ofpostseismic displacements used for the assimilation but also the improvement in the prediction skill of the following time series. Then, we appliedthe developed method to the observed GNSS time series for the first 15 d following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A-B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). The large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismicGNSS time series for the following 15 d. These characteristics can be also detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, isan effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

show abstract

Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses

Cited by 49 publications

References 102 publications

Numerical modeling of subduction: State of the art and future directions

Numerical modeling of subduction: State of the art and future directions

Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

Adjoint-based Direct Data Assimilation of Gnss Time Series for Optimizing Frictional Parameters and Predicting Postseismic Deformation Following the 2003 Tokachi-oki Earthquake

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