Shallow subsurface structure estimated from dense aftershock records and microtremor observations in Furukawa district, Miyagi, Japan

Goto, Hiroyuki; Mitsunaga, Hitoshi; Inatani, Masayuki; Iiyama, Kahori; Hada, Koji; Ikeda, Takaaki; Takaya, Toshiyasu; Kimura, Sayaka; Akiyama, Ryohei; Sawada, Sumio; Morikawa, Hitoshi

doi:10.1071/eg16113

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…It is generally believed that taking account of higher‐mode Rayleigh waves is effective in the context of strengthening constraints on the velocity structure model by using more types of data (e.g., Beaty et al, ; Beaty & Schmitt, ; Xia et al, ; Yokota et al, ). Many of available previous studies on microtremor array methods assumed, however, that the wavefield of vertical‐motion microtremors is composed of only fundamental‐mode Rayleigh waves, especially when the SPAC method was used (Chimoto et al, ; Giulio et al, ; Goto et al, ). Given that, Ikeda et al () showed that velocity structures can be estimated more accurately when higher‐mode Rayleigh waves are taken into account on the basis of the method of Tokimatsu (section ).…”

Section: Discussionmentioning

confidence: 99%

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A Bayesian Approach to Microtremor Array Methods for Estimating Shallow S Wave Velocity Structures: Identifying Structural Singularities

Cho

Iwata

2019

JGR Solid Earth

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This paper proposes applying Bayesian inference to the problem of estimating shallow S wave velocity structures-in particular, those containing a structural singularity (a low-velocity layer sandwiched between high-velocity layers or a high-velocity layer between low-velocity layers)-by using Rayleigh-wave phase velocities obtained in a microtremor survey. The method proposed here uses the empirical Bayesian approach, whereby field measurement data are used to build the prior distribution. An optimal velocity structure model is selected, from among multiple candidate models with different layer thickness parameter values, on the basis of a Bayes factor. The use of the proposed method allows the layer thickness parameter values in a one-dimensional velocity structure model to be determined objectively for explaining an observed Rayleigh-wave phase velocity dispersion curve. It also stabilizes the inversion process by the use of the prior distribution and strengthens constraints on the unknown parameter, or S wave velocity in each layer, by using multiple Rayleigh-wave modes. These characteristics of the proposed method allow flexible and stable inverse analysis of highly variable shallow subsurface structures. The present study conducts numerical experiments to show that our method allows the assumed structural singularities are reproduced. Then field measurement data from three sites are used to show that the method has allowed shallow structural singularities (depths 5-30 m) to be identified appropriately.Research on model selection in microtremor array methods has only started in recent years. Giulio et al. (2012) applied a quantity called the corrected Akaike information criterion (c-AIC;Hurvich & Tsai, 1989;Sugiura, 1978) to selecting a velocity structure model. When identifying parameter values that maximized the likelihood, however, they set limits on their range of parameter search and did not allow velocity reversal layers in some cases. That is not an appropriate way to use the c-AIC, because there is no way to count the number of "free" parameters, which is needed to calculate the c-AIC, under those limitations. Molnar et al. (2010), by contrast, avoided the problem by setting limits on their range of search and using those limits as the prior distribution for Bayesian inference. Dettmer et al. (2012) adopted a Bayesian CHO AND IWATA 527

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Section: Discussionmentioning

confidence: 99%

“…Further constraints are often imposed for evaluating a velocity‐structure model, such as velocities increase with depth (e.g., Giulio et al, ; Goto et al, ) to stabilize the inversion process when S wave velocities are set to be unknown.…”

Section: Methodsmentioning

confidence: 99%

A Bayesian Approach to Microtremor Array Methods for Estimating Shallow S Wave Velocity Structures: Identifying Structural Singularities

Cho

Iwata

2019

JGR Solid Earth

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“…This assumption seems quite common, in particular, for the spatial autocorrelation (SPAC) method (Aki, 1957), which has been widely adopted in recent years (e.g. Chimoto et al, 2016;Goto et al, 2017). However, higher modes are indeed easily excitable in shallow structures (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In some studies, V S (S-wave velocity) is fixed and only the layer thickness is estimated (e.g. Goto et al, 2017). When we use a model with few layers or a model with a fixed velocity in each layer, however, it is difficult to identify singular features involving a low-velocity layer sandwiched between high-velocity layers or a high-velocity layer between low-velocity layers (structural singularity), which are often seen in shallow velocity structures.…”

Section: Introductionmentioning

confidence: 99%

Development and numerical tests of a Bayesian approach to inferring shallow velocity structures using microtremor arrays

Cho

Iwata

2018

Exploration Geophysics

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Abstract. We propose an empirical Bayesian approach to inferring shallow (depth ranges from a few to several tens of metres) S-wave velocity structures using microtremor arrays and execute numerical tests to assess the feasibility of this approach. In our approach, the estimate of the S-wave structure (posterior) is derived from an empirical S-wave structure model (prior) and phase velocities of Rayleigh waves obtained with microtremor arrays. In other words, we aim to find a model that is close to the empirical model and is able to explain phase velocities with a 1D surface-wave theory. The inversion is stabilised by the constraints from the prior model so that model parameterisation with many thin layers can be adopted. The velocity structure is individually estimated for each of two cases (assumptions): the case where we assume fundamental-mode dominance and the case where we take into account the higher modes. Optimal values of the model parameters (e.g. a thickness parameter) are found, based on Akaike's Bayesian Information Criterion (ABIC), and the choice of the better assumption of the surface-wave theory is also based on ABIC. Numerical tests, where synthetic data is generated from a horizontally stratified two-layer model, indicate that the relative weight between a prior model and the observed data is appropriately adjusted by ABIC. It is revealed that a value of the thickness parameter required to reproduce the given two-layer model is successfully found by ABIC. We also suggest that we can make a plausible choice of the assumption of the surface-wave theory with ABIC, unless observation error is extremely large.

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High-resolution spatial elastic wave velocity variation detected using multiple-linear-array microtremor measurements

Chimoto,

Yamanaka

2024

Exploration Geophysics

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Shallow subsurface structure estimated from dense aftershock records and microtremor observations in Furukawa district, Miyagi, Japan

Cited by 6 publications

References 22 publications

A Bayesian Approach to Microtremor Array Methods for Estimating Shallow S Wave Velocity Structures: Identifying Structural Singularities

A Bayesian Approach to Microtremor Array Methods for Estimating Shallow S Wave Velocity Structures: Identifying Structural Singularities

Development and numerical tests of a Bayesian approach to inferring shallow velocity structures using microtremor arrays

High-resolution spatial elastic wave velocity variation detected using multiple-linear-array microtremor measurements

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