Abstract
Rapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.
Highlights
Rapid understanding of the magnitude of large earthquakes and their associated fault area is crucial for disaster response
To evaluate the performance of the proposed algorithm, we applied it to actual RTK-Global Navigation Satellite System (GNSS) displacement data, which recorded several past non-interplate large earthquakes, e.g., the 2016 Kumamoto (Mw 7.0) and 2019 Yamagata-Oki earthquakes (Mw 6.4)
We used the actual RTK-GNSS displacement data estimated by the REGARD system
Summary
Rapid understanding of the magnitude of large earthquakes and their associated fault area is crucial for disaster response. Ohta et al (2012, 2015) proposed an algorithm to detect and estimate permanent displacement due to the coseismic slip from real-time kinematic GNSS (RTK-GNSS) time series. They applied the developed algorithm to the 2011 Tohoku-Oki earthquake, and pointed out that the estimated moment release reached M w 8.7 within five minutes after the origin time, which is close to the actual moment magnitude (Mw 9.0). Numerous other efforts have shown the advantage of real-time GNSS data for the estimation of large events, including the studies of Hoechner et al (2013), Colombelli et al (2013), Melgar and Bock (2015), and Murray et al (2019)
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