Consistent estimation of strain-rate fields from GNSS velocity data using basis function expansion with ABIC

Okazaki, Tomohisa; Fukahata, Yukitoshi; Nishimura, Takuya

doi:10.1186/s40623-021-01474-5

Cited by 18 publications

(19 citation statements)

References 54 publications

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“…These requirements must be balanced with an increasing computational cost. In addition, some problems require careful treatment of basis functions at model boundaries (Fukahata & Wright, 2008;Okazaki et al, 2021). When continental-scale data are analyzed, basis functions must be set to conform to a spherical Earth (Tape et al, 2009).…”

Section: Model Regionmentioning

confidence: 99%

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

2022

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This study presents a new method of centroid moment tensor (CMT) data inversion to estimate time‐dependent regional stress fields. The Gaussian process (GP) is applied to resolve the computational difficulty of the existing basis function expansion method when analyzing high‐dimensional data, including time dependence. A critical step in the formulation is an analytical derivation of the relationship of the covariance function, which is a key ingredient of GP, between CMT data and the model stress field based on an observation equation. The validity and efficiency of the proposed method are verified through applications to CMT data in and around Japan after the 2011 Tohoku earthquake. The estimated stress field exhibits small‐scale heterogeneity in space and long‐term stability in time for most regions. Additionally, significant temporal variations are observed around the margin of the focal region of the 2011 event, with opposite changes on landward and oceanward sides. GP would be particularly effective for geophysical inversions of high‐dimensional data distributed over a broad region.

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Section: Model Regionmentioning

confidence: 99%

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

2022

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“…In other words, elastic deformation attributed to interplate coupling is fully released by interplate megathrust earthquakes; therefore, it can modulate the timing of crustal earthquakes but does not permanently load crustal faults (e.g., Hori and Oike, 1999;Mitogawa and Nishimura, 2020). Therefore, we used the corrected velocity after the removal of elastic deformation and estimated the strain rate distribution using the method proposed by Okazaki et al (2021), in which the velocity field is subjectively optimized by the expansion of the bicubic B-spline basis function.…”

Section: Gnss Datamentioning

confidence: 99%

“…These contrasting findings can be attributed to notable differences between the data analysis procedures. That is, we used a high-resolution distribution of the GNSS strain rate derived from a larger number of GNSS stations, optimal smoothing of the strain rate distribution (Okazaki et al, 2021), and a finer coupling model on the subducting plate interface (Nishimura et al, 2018). Moreover, the analyzed region was limited to southwest Japan in this study, but covers most of Japan in the work of Triyoso and Shimazaki (2012).…”

Section: Retrospective Testing Of the Forecast Modelsmentioning

confidence: 99%

Time-Independent Forecast Model For Large Crustal Earthquakes in Southwest Japan Using GNSS Data

Nishimura

2021

Preprint

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In this study, we developed a regional likelihood model for crustal earthquakes using geodetic strain rate data from southwest Japan. First, smoothed strain rate distributions were estimated from continuous GNSS measurements. Second, we removed the elastic strain rate attributed to interplate coupling on the subducting plate boundary, including the observed strain rate, under the assumption that it is not attributed to permanent loading on crustal faults. We then converted the geodetic strain rates to seismic moment rates and calculated the 30-year probability for M ≥ 6 earthquakes in 0.2 × 0.2° cells, using a truncated Gutenberg–Richter law and time-independent Poisson process. Likelihood models developed using different conversion equations, seismogenic thicknesses, and rigidities were validated using the epicenters and moment distribution of historical earthquakes. The average seismic moment rate of crustal earthquakes recorded during 1583–2020 was only 13–20 % of the seismic moment rate converted from the geodetic data, which suggests that the observed geodetic strain rate includes considerable inelastic strain. Therefore, we introduced an empirical coefficient to calibrate the moment rate converted from geodetic data with the moment rate of the earthquakes. Several statistical scores and the Molchan diagram showed that all models could predict real earthquakes better than the reference model, in which earthquakes occur uniformly in space. Models using principal horizontal strain rates exhibited better predictive skill than those using the maximum horizontal shear strain rate. There was no significant difference in the predictive skill between uniform and variable distributions for seismogenic thickness and rigidity. The preferred models suggested high 30-year-probability in the Niigata–Kobe Tectonic Zone and central Kyushu, exceeding 1% in more than half of the analyzed region. Model predictive skill was also verified by a prospective test using earthquakes recorded during 2010–2020. This study suggests that the proposed forecast model based on geodetic data can improve the regional likelihood model for crustal earthquakes in Japan in combination with other forecast models based on active faults and seismicity.

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“…To estimate the strain-rate eld, we use the surface displacement data of GEONET, a GNSS array by the Geospatial Information Authority of Japan (GSI), as in many previous studies (e.g., Sagiya et al 2000;Nishimura et al 2018;Fukahata et al 2020). In recent years, several methods have been developed for calculating strain rates from surface displacement data that can take into account the spatial heterogeneity of station spacing (Shen et al 2015;Sandwell and Wessel 2016;Okazaki et al 2021). With the development of automatic polarity picking based on machine learning, focal mechanisms of smaller earthquakes can be estimated more accurately than before (Uchide 2020).…”

Section: Introductionmentioning

confidence: 99%

Azimuthal differences and changes in strain rate and stress of the Japanese Islands deduced from geophysical data

Kosugi

Mitsui

2022

Preprint

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Geodetic and seismological observations have shown discrepancies between azimuths of maximum contraction of strain rates and maximum compression of stress. These discrepancies can be the results of the superposition of localized or transient mechanical processes such as fault coupling during seismic cycles. Rich sets of recent geophysical data allow us to conduct spatiotemporal imaging of the discrepancies. Here, we estimate the spatiotemporal evolution in the strain-rate fields of the Japanese Islands with optimized smoothing distances from 1997 to 2021 using Global Navigation Satellite System (GNSS) data, and investigate how the maximum contraction axes of horizontal strain rates differ from those of horizontal stress based on earthquake focal mechanisms. Several characteristic results are observed for each region within the Japanese Islands. Both azimuths of the strain rates and stress differ by more than 60 degrees over hundreds of kilometers from the Kanto region to along the Nankai Trough, related to seismotectonics due to the dual subduction of the Philippine Sea plate and the Pacific plate beneath the Japanese Islands. The azimuthal differences tend to be small along tectonic zones with active inland earthquakes and high strain rates on the back-arc sides. We also find that the 2011 off the Pacific coast of Tohoku earthquake caused notable azimuthal differences in the strain rates and the stress in the Tohoku region. Our dataset has wide spatiotemporal coverage and can serve as a basis for further research, for example, to estimate the current fault conditions during seismic cycles.

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Consistent estimation of strain-rate fields from GNSS velocity data using basis function expansion with ABIC

Cited by 18 publications

References 54 publications

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

Time Variable Stress Inversion of Centroid Moment Tensor Data Using Gaussian Processes

Time-Independent Forecast Model For Large Crustal Earthquakes in Southwest Japan Using GNSS Data

Azimuthal differences and changes in strain rate and stress of the Japanese Islands deduced from geophysical data

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