Fast crustal deformation computing method for multiple computations accelerated by a graphics processing unit cluster

Yamaguchi, Takuma; Ichimura, Tsuyoshi; Yagi, Y.; Agata, Ryoichiro; Hori, Takane; Hori, Muneo

doi:10.1093/gji/ggx203

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…This means that the advantage of addition of observation points in the offshore area is expected to be more pronounced with model error than illustrated in Figure 4. Additionally, the analyses with model error requires plenty number of calculations for Green's functions of various 3D heterogeneous structures with model error but we think it is possible to demonstrate with GPU application as in this study and also Yamaguchi et al (2017a), Yamaguchi et al (2017b).…”

Section: Results and Discussion For Coseismic Slipmentioning

confidence: 96%

“…As yet, few studies have sufficiently focused on the quantitative evaluation of the improvement in the accuracy of coseismic slip distribution and interseismic slip-deficit distribution estimations following the incorporation of such observational data. Previous studies that assess the detection ability enhancement through increasing the number of observation points do exist (e.g., Suito, 2016;Sathiakumar et al, 2017;Agata et al, 2019), while the effect of the model error in terms of the underground structure assumed in the data analysis on the estimation results has also been evaluated (Yamaguchi et al, 2017a;Yamaguchi et al, 2017b). However, few studies have quantitatively evaluated the effect of observation error on the estimation results of plate interface slip distribution focusing on the observation of the seafloor crustal deformation.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity Analysis for Seafloor Geodetic Constraints on Coseismic Slip and Interseismic Slip-Deficit Distributions

et al. 2021

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Estimating the coseismic slip distribution and interseismic slip-deficit distribution play an important role in understanding the mechanism of massive earthquakes and predicting the resulting damage. It is useful to observe the crustal deformation not only in the land area, but also directly above the seismogenic zone. Therefore, improvements in terms of measurement precision and increase in the number of observation points have been proposed in various forms of seafloor observation. However, there is lack of research on the quantitative evaluation of the estimation accuracy in cases where new crustal deformation observation points are available or when the precision of the observation methods have been improved. On the other hand, the crustal structure models are improving and finite element analysis using these highly detailed crustal structure models is becoming possible. As such, there is the real possibility of performing an inverted slip estimation with high accuracy via numerical experiments. In view of this, in this study, we proposed a method for quantitatively evaluating the improvement in the estimation accuracy of the coseismic slip distribution and the interseismic slip-deficit distribution in cases where new crustal deformation observation points are available or where the precision of the observation methods have been improved. As a demonstration, a quantitative evaluation was performed using an actual crustal structure model and observation point arrangement. For the target area, we selected the Kuril Trench off Tokachi and Nemuro, where M9-class earthquakes have been known to occur in the past and where the next imminent earthquake is anticipated. To appropriately handle the effects of the topography and plate boundary geometry, a highly detailed three-dimensional finite element model was constructed and Green’s functions of crustal deformation were calculated with high accuracy. By performing many inversions via optimization using Green’s functions, we statistically evaluated the effect of increase in the number of observation points of the seafloor crustal deformation measurement and the influence of measurement error, taking into consideration the diversity of measurement errors. As a result, it was demonstrated that the observation of seafloor crustal deformation near the trench axis plays an extremely important role in the estimation performance.

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Section: Results and Discussion For Coseismic Slipmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for Seafloor Geodetic Constraints on Coseismic Slip and Interseismic Slip-Deficit Distributions

et al. 2021

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“…The fault slip estimation methods in the next generation can be designed by considering the techniques of approximating the target PDF using an ensemble of sample models, narrowing down the model space by choosing candidate models from the ensemble, and incorporating realistic understructure models when generating the ensemble. Yamaguchi et al (2017) is an example of introducing stochastic modeling based on an ensemble of candidate underground structure models for fault slip estimation. This study calculated 1,000 coefficient matrices based on 1,000 different seismic velocity models and performed inversion analyses 1,000 times using each of the coefficient matrices.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference for fault slip distributions based on ensemble modeling of the uncertainty of underground structure

Agata¹,

Kasahara²,

Yagi³

2020

Preprint

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The prediction errors that originate from the uncertainty of underground structure is often a major contributor of the errors between the data and the model predictions in fault slip estimation using geodetic or seismic waveform data. However, most studies on slip inversions either neglect the model prediction errors or do not distinguish them from observation errors. Several methods that explicitly incorporated the model prediction errors in slip estimation, which has been proposed in the past decade, commonly assumed a Gaussian distribution for the stochastic property of the prediction errors to simplify the formulation. Moreover, the information on slip distribution and the underground structure is expected to be successfully extracted from the data by accurately incorporating the stochastic property of the prediction errors. In this study, we develop a novel flexible Bayesian inference method for estimating fault slips that can accurately incorporate non-Gaussian prediction errors. This method considers the uncertainty of the underground structure, including fault geometry based on the ensemble modeling of the uncertainty of Green's function. Furthermore, the framework allows the estimation of the posterior probability density function (PDF) of the parameters of the underground structure, by calculating the likelihood of each sample in the ensemble. To validate the advantage of the proposed method, we performed simple numerical experiments for estimating the slip deficit rate (SDR) distribution on a 2D thrust fault using synthetic data of surface displacement rates. In the experiments, the dip angle of the fault plane was the parameter used to characterize the underground structure. The proposed method succeeded in estimating a posterior PDF of SDR that is consistent with the true one, despite the uncertain and inaccurate information of the dip angle. In addition, the method could estimate a posterior PDF of the dip angle that has a strong peak near the true angle. In contrast, the estimation results obtained using a conventional approach, which introduces regularization based on smoothing constraints and does not explicitly distinguish the prediction and observation errors, included a significant amount of bias, which was not noticed in the results obtained using the proposed method. The experiments with different settings of the parameters suggested that inaccurate prior information of the underground structure with a small variance possibly results in significant bias in the estimation results, particularly the posterior PDFs for SDR, those for the underground structure, and the posterior predicted PDF of the displacement rates. The distribution shapes of the prediction errors for the representative model parameters in certain observation points are significantly asymmetric with large absolute values of the sample skewness, for which Gaussian approximation is not usually applied.

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Detectability analysis of interplate fault slips in the Nankai subduction thrust using seafloor observation instruments

et al. 2019

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Fast crustal deformation computing method for multiple computations accelerated by a graphics processing unit cluster

Cited by 9 publications

References 29 publications

Sensitivity Analysis for Seafloor Geodetic Constraints on Coseismic Slip and Interseismic Slip-Deficit Distributions

Sensitivity Analysis for Seafloor Geodetic Constraints on Coseismic Slip and Interseismic Slip-Deficit Distributions

Bayesian inference for fault slip distributions based on ensemble modeling of the uncertainty of underground structure

Detectability analysis of interplate fault slips in the Nankai subduction thrust using seafloor observation instruments

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