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Predicted results of the velocity structure at the target site of the blind prediction exercise from microtremors and surface wave method as Step1, Report for the experiments for the 6th international symposium on effects of surface geology on seismic motion
Earth, Planets and Space volume 75, Article number: 79 (2023)
Abstract
1D velocity profiles at a strong motion station in the northern part of the Kumamoto plain, Japan, were submitted in Step1 of the blind prediction exercise of strong ground motions in the sixth international symposium on effects of surface geology on seismic motion (ESG6). Individual participants were requested to estimate a 1D Swave velocity profile of sedimentary layers from the given data obtained by microtremor array explorations and surfacewave explorations at the site. This paper reports the target site, methods used by the individual participants, and the submitted results. More than half of the participants estimated the phase velocities of the Rayleighwave in the frequency range from 0.53 to 29.8 Hz. The statistical analysis of the phase velocity dispersion curves indicates that the standard deviation was below 40 m/s at the frequencies above 3.4 Hz, and it was below 20 m/s above 20 Hz. The Swave velocity profiles are also similar to a depth of 20 m. The standard deviation was below 45 m/s. The average Swave velocity in the top 30 m from the surface is 207.3 ± 60.7 m/s for the submitted profiles. The large variation is related to the introduction of the nearsurface low velocity layers. The large variation of the Swave velocities was found in the deep part. The average Swave velocity at a depth of 1500 m was 2674 m/s with the standard deviation of 786 m/s. We compared 1D amplifications for the submitted profiles. Common peaks can be identified at 0.3–0.4 Hz and 1–2 Hz, excluding two teams. However, the amplifications vary much in the frequency range higher than 4 Hz. Through the experiment, it was found that the dispersion curves and the shallow Swave velocity structures are estimated with a low standard deviation among the participants. Further development of the techniques for deep Swave velocity profiling was found to be required.
Graphical Abstract
Introduction
The blind prediction exercise of strong ground motions at a site on a sedimentary basin had been conducted in the last international symposia on effects of surface geology on seismic motion (Matsushima et al. 2023 in this special volume). The ground motions were estimated using Swave velocity (Vs) profiles and the observed ground motions at a rock site near the target site. This blind prediction exercise clearly indicates the importance of an accurate Vs model beneath and around the target site. Many exploration methods have accordingly been developed to know Vs profiles of shallow and deep sedimentary layers. The Vs exploration with microtremors (e.g., Okada 2003) have been popular particularly in the ESG (Effects of surface geology on seismic Motion)related studies because of easy filed operation and wellknown surfacewave theory including phase velocity inversions. Therefore, modeling of an Vs profile can be included as one of the important steps of the estimation of the ESG.
The blind prediction exercise in the sixth international symposium on effects of surface geology on seismic motion (ESG6) was planned to investigate accuracy and reliability of techniques to predict earthquake ground motions by actual estimations of unpublished strong motion records and an Vs profile at the target site in Kumamoto Prefecture, Japan (Matsushima et al. 2023). Participants of the blind prediction exercise of the ESG6 were requested to predict an Vs profile and earthquake ground motions in three steps. A 1D subsurface Vs profile was estimated from microtremor and surfacewave explorations data at the target site. They were, furthermore, requested to calculate weak and strong ground motions at the target site in the second and third steps (Tsuno et al. 2023 in this special volume). An earthquake record obtained during one of the aftershocks of the 2016 Kumamoto earthquake was selected as the target record in the second step. The strong motion records of the foreshock (14 April) and main shock (16 April) of the 2016 Kumamoto earthquake were then selected for the blind prediction in the third step.
This paper reports the results of the first step (Step1) of the blind prediction exercise of the ESG6. We compared the Vs profiles estimated by the participants. They were also compared with the Vs profile derived from a borehole near the target site which was open after the submission of the results of the Step1 by the participants.
Methods
Passive source and active source measurements were conducted at the target site (Fig. 1). Data acquisition is described in Matsushima et al. (2023). The passive measurements of microtremors were conducted for arrays of five different sizes (KUMSS1, S, SM, M, LL). Each array consists of two equilateral triangles and seven sensors were used. The combination of the side lengths of the two triangles of each array are (1, 2), (10, 20), (39, 78), (122, 243), and (481, 962) in meters for arrays SS1, S, SM, M, and LL, respectively.
We briefly introduce the methods used by the participants. The participants were requested to submit a dispersion curve and a structure model with a simple report. The results for the Step1 were submitted by 28 teams in total, as shown in Table 1. Fourteen teams participated from Japan, and three teams did from both France and Italy. Two teams participated from Taiwan and Mexico. From Australia, Israel, Iran, Greece and Switzerland one team participated or joined the teams. Several international teams are also included in the participants.
The method used by many teams to estimate phase velocity dispersion curves using the passive data was the SPAC (Spatial Autocorrelation Coefficient) method (Aki 1957; Okada 2003). The multimode SPAC method (MMSPAC; Asten and Hayashi 2018), the CCA (Centerless Circular Array) method (e.g., Tada et al. 2007) and the modified SPAC method (Bettig et al. 2001) are included in the SPAC method. The ESAC (Extended Spatial Autocorrelation; e.g., Ohori et al. 2002) was also used by three teams. Eight teams used the FK method to the passive data. The HighResolution FK (Capon 1969) method is included. The RTBF method (Wathelet et al. 2018) using three components were also used. Several teams used a combination of the methods. The three components highresolution FK (Poggi and Fäh, 2010) was also applied. The dispersion curves for the respective arrays using 3CHRFK and WaveDec (Maranò et al. 2012) were picked in the respective array resolution limits of the arrays. This code estimates the dispersion curves for Love and Rayleigh waves and the ellipticity angle for Rayleigh waves with the maximum likelihood approach.
Not only the vertical components, some teams also analyzed the horizontal components. One team conducted a reorientation of the sensors towards the north direction using a crosscorrelation analysis of the horizontal components with respect to a reference station in a frequency range of 0.1 to 0.3 Hz. They have reported misorientations of up to 48°. Two teams used a crosscorrelation technique, and one team used only the horizontaltovertical spectral ratio instead of the dispersion curve (e.g., Nagashima et al. 2014).
Ten teams used the active source data for the estimation of the dispersion curve. Every team of this group used the FK method in the analysis and the MASW analysis (Park et al. 1999). The effect of the higher modes of the surface waves was also included in the analyses. Many teams used the software of Geopsy (e.g., Wathelet et al. 2020) for the active and passive data, but some teams used the software of BIDO (Tada et al. 2010) in the passive data analysis. The others used inhouse softwares. The horizontaltovertical spectral ratio (Several abbreviations are used by teams such as H/V, HVSR, MHVR) was also often used by many participants. The H/V was generally estimated by sensor No. 4 for the Marray or No. 3 for the SMarray. Many teams found two significant peaks in the H/V curve. The predominant peak appears at frequencies between 1.0 to 1.5 Hz. Another peak appears at frequencies between approximately 0.3 and 0.35 Hz. Several participants specified that the predominant frequencies are 1.2, 1.25 and 1.3 Hz. Some teams also identified the deep trough at a frequency of approximately 3.0 Hz. One team conducted a polarization analysis (Burjánek et al. 2010, 2012) and they did not find any signs for twodimensional polarization effects, whereas another team noted that the 2D effect is observable in the H/V of the large array.
Various techniques were used for the inversion of the dispersion curve. Some teams used primitive techniques of trialanderror or random search. Some other teams used iterative inversions or nonlinear neighborhood algorithm in Geopsy (e.g., Wathelet et al. 2005), a neighborhood algorithm (e.g., Sambridge 1999; Wathelet et al. 2004) or the Conditional Neighborhood algorithm (Wathelet et al. 2008). Monte Carlo (MC) and modified MC (Socco and Boiero 2008) techniques were also used. Ten teams adopted a heuristic inversions of Genetic Algorithm (GA; Yamanaka and Ishida 1996) or Simulated Annealing (SA). Three teams applied a hybrid method of GA and SA (Yamanaka 2007). The latest techniques such as Bayesian approach (Cho and Iwata 2019) and Markov Chain Monte Carlo (MCMC; e.g., Goodman and Weare 2010; ForemanMackey et al. 2013) were also used. One team directly converted the phase velocity in the frequency domain as a velocity structure in the depth domain. One team inverted the Vs structure using the method proposed by Herrmann (1991). The participants who also estimated Lovewave dispersion curve used it for a joint inversion.
More than 21 teams reported that they have also estimated H/V curves. Furthermore, the joint inversion of the dispersion curve with the H/V curve were performed by more than nine teams (e.g., Hobiger et al. 2009; Albarello et al. 2011; GarcíaJerez et al. 2016; PiñaFlores et al. 2017; GarcíaJerez and PiñaFlores 2018). Even the H/V curve was not incorporated in the inversion, more than six teams used the H/V curves to check the validity of their estimated profiles. However, the theory behind the forward modeling of the H/V curve varies from team to team. Twelve teams modeled the H/V as an ellipticity of Rayleighwave (Nogoshi and Igarashi 1971). Seven teams modeled it with a diffuse field concept or assumption (SánchezSesma et al. 2011; Kawase et al. 2011, 2017). One team assumed the idea of Arai and Tokimatsu (2004, 2005), which takes the effects of the fundamental and higher modes into account.
Several teams specified the relationships between V_{P}, density and V_{S} during the inversion. Some used the relationship proposed by Ludwig et al. (1970) and some used Kitsunezaki et al. (1990). Other teams specified that they used results by Brocher (2005), Gardner et al. (1974) and Ohta and Goto (1978). Others used the constant values defined in existing models JSHIS (NIED 2019) and JIVSM (Koketsu et al. 2009, 2012).
The number of layers assumed in the inversion was generally six to ten. One team assumed eighty layers. Another team modeled over a thousand layers with a thickness interval of 2.5 m. Some teams set initial models from the existing models, JSHIS V2 (NIED, 2019) and JIVSM (Koketsu et al. 2009, 2012) for the deep part and KuniJiban (Kunijiban 2022) and Chimoto et al. (2016) for the shallow part.
Results
Figure 2 shows the dispersion curves submitted by all the teams. Individual curves are plotted one by one in Additional file 5: Fig. S1 which is reordered from Table 1 to maintain anonymity. Three teams estimated the effective mode of Rayleighwave (e.g., Tokimatsu et al. 1992; Asten et al. 2019) shown by bold curve and assumed it for the inversion. The others estimated the fundamental mode of Rayleighwave. Two teams estimated higher modes of Rayleighwave as well. Two teams submitted fundamental mode of Love wave from the horizontal passive data.
Most of the submitted phase velocities are distributed around the theoretical value calculated for the preferred velocity model which is made by Matsushima et al. (2023). The difference among the methods is not observed clearly. We observe four significant outliers of the estimated dispersion curves in the figure. Two of them are similar to each other, even they used different methods. Both values are approximately double the values estimated by most teams. We suppose that these two teams made mistakes in the input parameters during the processing of the provided data. One dispersion curve is totally lower than the others. Except for these four curves, all the dispersion curves show similar values and are also similar to the curve calculated theoretically using the preferred velocity model.
We statistically analyzed the submitted dispersion curves. Except for one team who used only the active data, all the teams estimated the dispersion curves in the frequency ranges of 2–5 Hz, as shown in Fig. 3. More than half of the participants estimated the phase velocities in the frequency ranges from 0.53 to 29.8 Hz. Figure 3 also shows the average of the dispersion curves with the standard deviation. The lower the frequency of the phase velocity, the bigger the standard deviation. The average values and the standard deviations of the phase velocities are 2283.4 ± 419.0 m/s at 0.53 Hz, 1322.6 ± 317.0 m/s at 1.00 Hz, 507.6 ± 119.7 m/s at 2.01 Hz, 186.9 ± 35.7 m/s at 5.03 Hz, and 156.1 ± 21.0 m/s at 29.78 Hz. The average curve exhibits higher values than the theoretical one for the preferred velocity model in the frequency ranges of 0.5–0.6 and 0.8–3 Hz. We are still not sure if the reason of this difference is caused by the mixture of the higher modes or not.
Because we found the four outliers in the dispersion curves in Fig. 2, we also analyzed statistically the dispersion curves by excluding these curves. The averaged curve excluding four outliers changed significantly in the frequency range below 1 Hz. However, it did not show significant change in the high frequency range.
Figure 4 illustrates the frequency ranges, where the phase velocities were estimated by teams. It is ordered in random to maintain anonymity. It is inferred from the figure that all the teams estimated the phase velocities at the lowest frequencies from the LLarray. The minimum and maximum frequencies are significantly depending on the teams. From the analyses of only the passive data, one team estimated the phase velocity at the frequency of approximately 40 Hz. One team estimated the phase velocity at the frequency of approximately 0.2 Hz.
The teams used the active source data estimated the phase velocity at high frequencies above 10 Hz. The maximum frequency of the estimated phase velocity varies from team to team. Even the SS1array has the smallest size of the array with sidelengths of 1 and 2 m, only five teams used the data of SS1. The SS1 was placed on the same kind of ground with the Sarray. Most of the teams estimated the phase velocities at frequencies of 4 to 6 Hz from the data of the Sarray, where the velocities are low and constant. One team comments on this point, “We didn’t use the SS1array to calculate phase velocity due to the extremely small radius that may only have a resolution corresponding to a very highfrequency signal using the F–K method”. Another team said, “the estimated phase velocity curves obtained from this array tended to be different from it obtained from other arrays, thus it was excluded from the analysis.” Another team said, “The miniature array SS1 yields frequencies up to 40 Hz on the MMSPAC curves but the layered earth model produced includes an apparent nearsurface compact layer (Vs_{1} = 479 m/s). However, this layer is not permitted by the MMSPAC curves of array S, hence the array SS1 was discarded.”
All the submitted Vs profiles are shown in Fig. 5 with the models estimated previously. Individual curves are plotted one by one in the supplementary file (Additional file 6: Fig. S2), which is ordered in the same way, as shown in Additional file 5: Fig. S1. At a depth of 1 m, the average Vs among all the participants is 170.8 ± 43.2 m/s, whereas the value of the preferred velocity model is 95 m/s. The preferred model in the shallow part was constructed from the result of the PSlogging. Only a few teams detected the low velocity layers with a Vs below 100 m/s. Even though, their depths are shallower than that of the PSlogging. Looking at the individual curves (Fig. 2), several curves show the similar value with that for the preferred model, whereas the averaged phase velocities for all the participants (Fig. 3) are different from the preferred model. One team, who did not use any dispersion curve for the Vs modeling, detected the low velocity layer. The average Vs is 195.5 ± 46.6 m/s (100 m/s for the PSlogging) at a depth of 5 m, and it becomes 205.1 ± 44.0 m/s at a depth of 10 m (190 m/s for the PSlogging). The Vs values of the preferred velocity model and the averaged model are comparable at the depths of about 10 to 20 m. The average Vs is 305.6 ± 102.3 m/s at a depth of 30 m (260 m/s for the PSlogging). The standard deviation (S.D.) of the Vs until a depth of 30 m from the surface is not large, whereas the S.D. becomes large below 30 m. Similar trend can be seen at depths from 30 to 50 m in both the preferred model and the averaged model. The Vs values of the preferred model are within the variation of the S.D. at these depths. It should be noted that the PSlogging data are not available below 40 m depth, to construct the preferred velocity model.
The deep part below 50 m has much larger variation of the Vs. Until a depth to 600 m, Vs of the preferred model are generally lower than the average values. The Vs values of the basement are different among the teams. However, most of the teams estimated shallower depth to the layer with a Vs of above 3 km/s. The basement depth is about 1.5 km in the preferred model, whereas many teams estimated the depth of approximately 1.3 km. The difference due to the methods is also not observable.
We quantitatively compared the models by individual participant with the preferred velocity model. The average of absolute percentage errors (APE) of the Vs at the ith depth of the submitted jth model \({Vs}_{i}^{j}\) and the preferred model \({Vs}_{i}^{P}\) was defined as
where M is a number of the Vs to be compared. Left of Fig. 6 shows the APEs for the Vs to the depth of the bottom layer of each model. The APEs for most of the submitted models are from 20 to 40% with an average value of 30%, except for the outlier models. The right panel of the figure also shows the APEs for a depth to the top of the layer with the Vs more than 400 m/s. The APEs for the shallow parts have the similar distributions as those of the deep layers in the left panel.
It is interesting to focus on the timeaverage Vs to the depth to 30 m (Vs30) considering a relation with the ground motion amplification. The Vs30 is 194.6 ± 36.1 m/s in the average model, whereas the value from the PS logging is 162.8 m/s. Even excluding four outliers found in the dispersion curves and the model by a team who used only the active source data, the average Vs30 of the submitted models becomes 185.1 ± 22.4 m/s. The difference seems a bit large. This difference may have been caused by the difference in the upper several meters, where most of the teams did not detect the very low velocity layers as mentioned above. The timeaverage Vs to the depth of 5 m is 171.5 ± 28.8 m/s and it is 180.0 ± 25.8 m/s to 10 m depth (Fig. 7). Compared with that for the preferred model, the timeaverage Vs to the depth of 5 m is more than 1.7 times larger than that of the preferred model. This difference could be possibly, because the result of the PSlogging is in error due to an instrumental issue, or due to the change in nearsurface ground condition.
Timeaverage Vs (Garofalo et al. 2016) is also computed. The timeaverage Vs in the topmost z meters is defined as
in which N is the number of layers used for the discretization of the model from the surface to z, and H_{i} and V_{S,i} are the thickness and Vs for each layer i, respectively. The value for z of 30 m is referred to as the Vs30, for example. Figure 8 shows the timeaverage Vs. It seems that the variation of the V_{s,z} becomes gentle. However, V_{s,z} of the preferred velocity model is generally lower than those of the submitted models. The distribution of Vs_{,100}–Vs_{, 200} seems to follow a normal distribution. However, the Vs,z below 1250 m seems to be clustered in three groups. Two teams show the Vs of approximately 1.9 km/s at a depth of 1.5 km. They are the teams who estimated significantly higher phase velocity than others (see Fig. 2). Some teams show approximately 1 km/s at a depth of 1.5 km, and many other teams show approximately 1.3 km/s. These teams did not estimate the high Vs layers, which corresponds to the seismic bedrock.
In the shallow part, the V_{s,z} of the models are significantly faster than that of the preferred model. This result could be inferred from the differences found in the dispersion curves as seen above.
Discussion
The site effects would be significantly important in the prediction of the ground motion at the target site. We, therefore, discuss about the 1D Swave amplification characteristic computed for the submitted structure models. Figure 9 shows the computed 1D amplification using the Haskell’s matrix method. Infinite Q values are assumed for all the layers in the calculation. It is noted that we used all the layers in the submitted models in the calculation. This means that the Swave velocities of the bottom layers are different among the models. The amplification computed for the preferred velocity structure model is also shown in the figure. Except for one curve, all the amplifications show large amplification values at frequencies between 1 and 2 Hz. Many curves show peaks at the frequency range from 0.3 to 0.4 Hz. In the high frequency range above 4 Hz, the amplification values fluctuate around the amplification value of about ten. However, these trends are similar among all the curves. The averaged amplification characteristic among all the amplifications exhibits similar trends with that for the preferred model. However, the amplification for the preferred velocity model exhibits a bit larger value than the averaged one in the entire frequency band. Furthermore, we calculated the APEs for the amplifications for the individual model with that of the preferred velocity model, as shown in Fig. 10. Most of the models have errors of 30–40%, which is slightly larger than those of the Swave velocity structure models in Fig. 6.
Conclusions
This paper explains the methods used by the individual participants, and the results in the Step1 of the blind prediction exercise in ESG6. The phase velocity dispersion curve of Rayleighwave in the frequency range from 0.8 to 15 Hz was estimated by most of the participants. The comparison of the dispersion curves indicates that they are very similar in the frequency range from 3 to 40 Hz. Most of the submitted dispersion curves in the frequency range from 0.6 to 40 Hz are similar to the theoretical one for the velocity model derived from the velocity logging. The estimated phase velocities at frequencies lower than 0.8 Hz are larger than the theoretical one. The submitted Vs profiles are also similar to the depth of 50 m. It is, therefore, concluded that the Vs30 can be reliably estimated from active and passive surfacewave data. However, the estimated dispersion curves are larger than that of the logging profile. The large variation of the Vs is identified in the deep part because of the large variation of the phase velocities at low frequencies. These results clearly indicate high accuracy of the existing techniques for the shallow Swave velocity profiling and further need to develop techniques for more reliable deep Vs profiling (Additional files 1, 2, 3, 4).
Availability of data and materials
We provide the digital data of the dispersion curve and the Swave velocity structure that are averaged for the data submitted by the participants as supplementary files. The digital data of the curves that were submitted by participants are not available.
Abbreviations
 ESG:

Effects of Surface Geology on Seismic Motion
 SPAC:

Spatial Autocorrelation Coefficient
 CCA:

Centerless Circular Array
 ESAC:

Extended Spatial Autocorrelation
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Acknowledgements
The authors gratefully thank for those who participated Step1 for the blind prediction exercise. The participants are Diego Mercerat, ChunHsiang Kuo, CheMin Lin, JyunYan Huang, YinTung Yen, MingChe Hsieh, ChunTe Chen, Michael Asten, Shaghayegh Naghshineh, Aysegul Askan, Yaniv Darvasi, Saeid Soltani, Enrico Paolucci, Ikuo Cho, Hiroyuki Goto, Sebastiano Foti, Tomotaka Iwata, Michihiro Ohori, Hiroyuki Kosaka, Mitoshi Yasui, Nobuhide Narita, Takeshi Yamamoto, Eva Riga, Maria Manakou, Zafeiria Roumelioti, HueyChu Huang, ChengFeng Wu, TienHan Shih, Haruhiko Suzuki, Fumiaki Nagashima, Eri Ito, Hiroshi Kawase, Kenichi Nakano, Francisco José SánchezSesma, Marcela BaenaRivera, Hugo CruzJimenez, Mathieu Perton, Mario Alfredo Ortega Rodríguez, Alfredo Dávila García, Galaviz Alberto, Cesar Ulises López Torres, Antonio Garcia Jerez, Jorge Aguirre and Team UNAM, Donat Fäh, Manuel Hobiger and ETH Zurich Team, Hiroaki Yamanaka, Kosuke Chimoto, Seiji Tsuno, Shigeki Senna, Takahiro Maeda, Asako Iwaki, Nobuyuki Morikawa, Hisahiko Kubo, Shin Aoi, Hiroyuki Fujiwara, Hiroaki Sato, Sadanori Higashi, Shinichi Matsushima, Mona Izadi, Takashi Hayakawa, Kenichi Tsuda, Mitsutaka Oshima, Cécile Cornou, Giuliano Milana, Salomon Hailemikael, Giuseppe Di Giulio, Paola Bordoni, Nobuo Takai, Tatsuo Kanno and Michiko Shigefuji. The authors also acknowledge all support and cooperation provided to conduct the surveys and all members of the ESG6 local organizing committee as well as the members of the research committee on strong motion evaluation of JAEE. The members of WGPM were Prof. Hiroaki Yamanaka, Dr. Shigeki Senna, Dr. Takumi Hayashida of Building Research Institute, Dr. Shusuke Oji of Chuo Kaihatsu Corporation and Dr. Yoshiaki Inagaki of OYO Corporation. The authors are grateful to the editors Dr. Aitaro Kato, Dr. Hideo Aochi, a reviewer Dr. Michael Asten and an anonymous reviewer for their constructive comments that significantly contribute to improve the manuscript.
Funding
This study was supported by CoretoCore Collaborative Research between Earthquake Research Institute, The University of Tokyo and Disaster Prevention Research Institute, Kyoto University, Grant Nos. 2019K04 (20192020), 2021K03 (2021), 2022K05 (2022), and JSPS KAKENHI 22H00234.
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The structure of the manuscript was decided by YH, ST, SM and KC. All authors reviewed the manuscript draft and revised it. All authors read and approved the final manuscript.
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Additional file 1.
Data of the dispersion curve that are averaged for all the results submitted by the participants and their standard deviation.
Additional file 2.
Data of the dispersion curve that are averaged for all the results submitted by the participants excluding four outliers and their standard deviation.
Additional file 3.
Data of the Swave velocity structure that are averaged for all the results submitted by the participants and their standard deviation.
Additional file 4.
Data of the Swave velocity structure that are averaged for all the results submitted by the participants excluding four outliers and their standard deviation.
Additional file 5: Fig. S1
. Each dispersion curveshowing on all the submitted curves. Grey is the preferred model for Rayleighwave fundamental mode.
Additional file 6: Fig. S2.
Each Swave velocity structure modelshowing on all the submitted models. Gray indicates the preferred model.
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Chimoto, K., Yamanaka, H., Tsuno, S. et al. Predicted results of the velocity structure at the target site of the blind prediction exercise from microtremors and surface wave method as Step1, Report for the experiments for the 6th international symposium on effects of surface geology on seismic motion. Earth Planets Space 75, 79 (2023). https://doi.org/10.1186/s40623023018423
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DOI: https://doi.org/10.1186/s40623023018423