Use of Fourier Phase Characteristics and Effective Stress Analyses: A Result for the ESG6 Blind Prediction Steps 2&amp;3

doi:10.21203/rs.3.rs-2122690/v1

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Use of Fourier Phase Characteristics and Effective Stress Analyses: A Result for the ESG6 Blind Prediction Steps 2&3

https://doi.org/10.21203/rs.3.rs-2122690/v1

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published 20 Jun, 2023

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This article describes the methods employed by the author and the results for the ESG6 Blind Prediction Steps 2&3. The target event for Step 2 was an M5.9 earthquake in the Aso region, Japan. To simulate ground motions at the target site “KUMA”, the author used the site-effect substitution method. The Fourier amplitude spectrum was estimated from the spectral ratio between KUMA and a nearby station JMA. The Fourier phase spectrum was approximated by the spectrum of another event at KUMA. Comparison between the estimated and recorded ground motions after the blind prediction revealed that, except for the error in the arrival time, the estimated ground motions were fairly consistent with the observed ground motions, indicating the effectiveness of the site-effect substitution method when the rupture process of the target event is simple and the soil nonlinearity at the target site is not significant. The target events for Step 3 were the M6.5 foreshock and the M7.3 mainshock of the 2016 Kumamoto earthquake sequence. To simulate ground motions at KUMA, the author used a similar method to estimate the Fourier amplitude spectrum, however, the author simply used the Fourier phase spectrum at JMA for the target events. Then, the author conducted effective stress analyses using a program called “FLIP”. The parameters were determined from the PS logging results, N values and fine particle content. Comparison between the estimated and recorded ground motions after the blind prediction indicated that, although the estimated ground motions captured the main phases of the recorded ground motions, the low frequency components were overestimated and the high frequency components were underestimated. The strong soil nonlinearity considered in the effective stress analyses was the main cause of the discrepancy between the estimated and observed ground motions. One explanation for this result could be that the nonlinear soil behavior at KUMA during the foreshock and the mainshock was not a strong one. Another explanation could be that the effect of soil nonlinearity was already included in the records at JMA and the effect of soil nonlinearity was double counted in the results submitted by the author.

This article describes the methods employed by the author and the results for the ESG6 Blind Prediction (Matsushima et al., 2022; Chimoto et al., 2022; Tsuno et al., 2022) Steps 2&3. The goal of Step 2 was to estimate ground motions at a target site called “KUMA” (32.7756N, 130.6879E) during an M5.9 target event that occurred approximately 1.5 hours after the mainshock of the 2016 Kumamoto earthquake sequence. In this article, all the magnitude are those determined by the Japan Meteorological Agency (JMA). For this step, the organizer of the blind prediction provided us with aftershock records at the same site, which enabled us to evaluate the site amplification factor at the target site empirically. Therefore, the author used the site-effect substitution method (Hata et al., 2011), which is advantageous when aftershock records are available. The goal of Step 3 was to estimate ground motions at the same site during the M6.5 foreshock and the M7.3 mainshock of the Kumamoto earthquake sequence. For this step, the organizer of the blind prediction suggested us to pay attention to the effects of soil nonlinearity. Therefore, the author followed the following procedure: (1) Estimate surface ground motions in which linear soil response is assumed. (2) Estimate 2E ground motions at GL-31m assuming linear multiple reflections. (3) Estimate surface ground motions with effective stress analyses. For (3), the author used a program called “FLIP” (Iai, 1991; Iai et al., 1992), which has been extensively used in the design of port structures in Japan. The parameters were determined based on a simplified procedure (Morita et al., 1997) from the PS logging results, N values and fine particle content provided by the organizer (Matsushima et al., 2022). After the blind prediction, the records at KUMA for the target events were distributed by the organizer. This article also shows the comparison between the estimated and recorded ground motions and discusses some lessons learned from the blind prediction.

Step 2 – Simulation Of Weak Motions

The goal of Step 2 was to estimate ground motions at a target site called “KUMA”, located at 32.7756N, 130.6879E, shown in Fig. 1, during the M5.9 target event that occurred in April 16, 2016, 3:03 (JST). The target event (No.35 in Table 1 and Fig. 1) occurred in the Aso region.

For this step, the organizer of the blind prediction provided us with aftershock records at KUMA, which enabled us to evaluate the site amplification factor at the target station empirically. Therefore, the author used the site-effect substitution method (Hata et al., 2011), which is advantageous when aftershock records are available. The method assumes that the target event has not been observed at the target site, but has been observed at a nearby reference site. The method also assumes that several events different from the target event have been observed at both sites. Then the Fourier amplitude spectrum at the target site for the target event can be estimated as a product of the Fourier amplitude spectrum for the target event observed at the reference site and the Fourier spectral ratio between the two sites estimated from available events. The Fourier phase spectrum at the target site for the target event is approximated by the Fourier phase spectrum at the target site for a selected event.

Reference site

Ground motion data at KUMA were provided by the organizer for all the events listed in Table 1 except for the event No.35, which was the target event. The organizer also provided ground motion data at a station called “SEVO”, shown in Fig. 1. However, the author decided to use another station operated by JMA (32.7866N, 130.6876E) as a reference station (the station will be abbreviated as JMA), because the station is closer to KUMA as shown in Fig. 1. In fact, it is only 1.2 km north of KUMA. Table 1 shows the availability of records at JMA.

Fourier amplitude spectrum

Figure 2 shows the Fourier amplitude spectra observed at KUMA and JMA for five events with M ≥ 4.5 (No.64, No.104, No.109, No.205, No.206) for the horizontal components. The Fourier spectra are in terms of the vector sum of two horizontal components, smoothed with a Parzen window of 0.05 Hz. The Fourier spectra are shown for events with M ≥ 4.5 because the records in this range of magnitude typically exhibit good S/N ratio down to 0.2 Hz. In Fig. 2, we find a robust tendency for the frequency components of 0.5–1.2 Hz to be greater for KUMA than for JMA. The top left panel of Fig. 3 shows the Fourier spectral ratios between KUMA and JMA for the five events. The spectral ratios are greater than one for the frequency components of 0.5–1.2 Hz. The top right panel of Fig. 3 shows the geometric mean of the Fourier spectral ratios. In general, Fourier spectral ratios between different stations can depend on the location of the source. From such a point of view, it should be noted that the target event No.35 occurred in the Aso region (Fig. 1). On the other hand, the five events can be categorized into two groups: No.104, No.205 and No.206 occurred in the Aso region; No.64 and No.109 occurred closer to the target site (Fig. 1). Therefore, the averaged ratio for the five events was compared with the averaged ratio for the three events that occurred in the Aso region in the top right panel of Fig. 3. As a result, the averaged ratio for the five events was not significantly different from that for the three events. Therefore, the author decided to use the averaged ratio for the five events.

It should be noted that the target event No.35 occurred approximately 1.5 hours after the mainshock. The organizer of the blind prediction suggested us to pay attention to the effects of soil nonlinearity for the mainshock at KUMA. Therefore, the effects of soil nonlinearity such as the remaining effects of excess pore water pressure might also be anticipated for the target event at KUMA. Furthermore, the S-wave amplitude was relatively small compared to the P-wave amplitude for the event No.43, which was the earliest available event after the mainshock at KUMA (Table 1), which could also be indicative of soil nonlinearity. For this reason, the author compared the spectral ratio for the event No.43 with the averaged spectral ratio in the bottom panel of Fig. 3. As a result, the author found no strong evidence of soil nonlinearity for the event No.43, which occurred approximately 2.5 hours after the mainshock (Table 1). Therefore, the author assumed that there was no strong nonlinear effect at KUMA during the target event that occurred approximately 1.5 hours after the mainshock.

The spectral ratio for the vertical component was evaluated in a similar way. The results are shown in Fig. 4. Again, the averaged ratio for the five events was not significantly different from that for the three events. Therefore, the author decided to use the averaged ratio for the five events.

Fourier phase spectrum

In general, Fourier phase characteristics of a ground motion significantly depend on the location of the source. It is obvious from Fig. 1 that, among available events, the event No.104 was closest to the target event No.35. Therefore, the author decided to approximate the Fourier phase spectrum at KUMA for the target event with the Fourier phase spectrum at the same station for the event No.104.

To confirm the appropriateness of this decision, the author investigated the similarity of the Fourier phase characteristics between the target event and the event No.104 at JMA. Figure 5 compares the observed velocity waveforms for the target event at JMA and the phase-exchanged velocity waveforms in which the Fourier amplitude of the target event and the Fourier phase of the event No.104 were combined. The waveforms (black and red) share the same Fourier amplitude but have different Fourier phase characteristics. Figure 5 indicates that the waveforms are similar, indicating the similarity of the Fourier phase characteristics between the target event and the event No.104 at JMA. The result supported the decision to use the Fourier phase spectrum of the event No.104 at KUMA in the site-effect substitution method.

The author applied the site-effect substitution method. The horizontal components were estimated based on the horizontal spectral ratio (Fig. 3). The vertical component was estimated based on the vertical spectral ratio (Fig. 4).

In Fig. 6, the acceleration waveforms estimated and submitted by the author for the blind prediction Step 2 are compared with the recorded acceleration waveforms, which were distributed by the organizer after the blind prediction. The origin of the horizontal axis corresponds to April 16, 2016, 3:03:10 (JST). It can be pointed out that there is an error in the arrival time of the main energy. When the author submitted the results, the author did not make any rigorous investigation in terms of the origin of the time history. After the recorded waveforms were distributed, the author found that, although the events No.35 and No.104 occurred at similar locations (Fig. 1 and Table 1), the arrival of the S waves were delayed for the event No.35 compared to the event No.104 both for KUMA and JMA. The author cannot find any good explanation for this tendency. Therefore, in this article, the author decided to simply shift the estimated waveforms so that the arrival time of the S wave be consistent between the estimated and recorded waveforms. Figure 7 compares the estimated acceleration waveforms after the time shift with the recorded acceleration waveforms. Figure 8 compares the estimated velocity waveforms after the time shift with the velocity waveforms calculated from the records. Figure 9 compares the estimated Fourier spectra with the Fourier spectra calculated from the records. These comparisons reveal that, except for the error in the arrival time, the estimated ground motions were fairly consistent with the observed ground motions. Especially the velocity waveforms were reproduced well including the later phases. The result indicates the effectiveness of the site-effect substitution method in estimating ground motions after an earthquake at a site where the ground motions are unknown as long as aftershock records are available at the site.

It should be noted, however, that two condition contributed to this favorable result in Step 2. One is the fact that the rupture process of the target event was simple. As can be seen in Fig. 5, the Fourier phase spectra were interchangeable between the events No.35 and No.104. This means that the Fourier phase characteristics were almost determined by the path and site effects and not by the source effects. This is the evidence for a simple rupture process of the target event No.35. Another condition was that the soil nonlinearity at the target site was negligible. This was already discussed in terms of the bottom panel of Fig. 3 and supported by the accurate reproduction of the Fourier spectra in Fig. 9. These conditions were favorable for the application of the site-effect substitution method.

Step 3 – Simulation Of Strong Motions

The goal of Step 3 was to estimate ground motions at KUMA during the M6.5 foreshock and the M7.3 mainshock of the Kumamoto earthquake sequence. For this step, the organizer of the blind prediction suggested us to pay attention to the effects of soil nonlinearity. Therefore, the author followed the following procedure:

(1) Estimate surface ground motions in which linear soil response is assumed.

(2) Estimate 2E ground motions at GL-31m assuming linear multiple reflections.

(3) Estimate surface ground motions with effective stress analyses.

For (1), the author estimated the Fourier amplitude and phase characteristics separately. For (3), the author used a program called “FLIP” (Iai, 1991; Iai et al., 1992). This procedure implies that the effect of soil nonlinearity at the reference site JMA, if any, is neglected. This fact will be discussed later.

Applicability of the site-effect substitution method

There were several candidate methods to estimate ground motions during the foreshock and the mainshock including ground motion simulations based on fault models. Among them, the author first examined the applicability of the site-effect substitution method, which was used in Step 2.

One of the main features of the site-effect substitution method is that the Fourier phase spectrum at the target site for the target event is approximated by the Fourier phase spectrum at the same site for another event. This operation is effective if the two events share the same path and site effects and the source effect is not a dominant factor for the Fourier phase spectra. These conditions applied to Step 2. However, it was anticipated that the latter condition does not apply to Step 3 due to the complexity of the rupture process especially for the mainshock (Asano and Iwata, 2016; Nozu and Nagasaka, 2017). Therefore, the author investigated the similarity of the Fourier phase characteristics between the target events and other available events at JMA.

In Fig. 10, the similarity of the Fourier phase characteristics is examined between the mainshock and the event No.161 at JMA. Among available events, the event No.161 produced the best results for the mainshock. This is presumably because the event No.161 (Fig. 1) occurred near the asperities of the mainshock (Asano and Iwata, 2016; Nozu and Nagasaka, 2017) and this event conveyed similar path and site effects with the mainshock. However, the large-amplitude phase for the EW and UD components at approximately 27 s was not captured by the phase-exchanged waves. This is presumably because of the complexity of the rupture process of the mainshock. Therefore, the author avoided to use the site-effect substitution method for the mainshock.

The similarity of the Fourier phase characteristics was also examined between the foreshock and other available events at JMA (Figs. 11 and 12). Among available events, the events No.64 and No.161 produced the best results for the foreshock, presumably because these events occurred near the asperities of the foreshock (Asano and Iwata, 2016) and they conveyed similar path and site effects with the foreshock. The similarity of the waveforms indicated the potential applicability of the site-effect substitution method to the foreshock. However, the author avoided to use the site-effect substitution method for the foreshock simply because the author preferred to use the same method for the foreshock and the mainshock.

Availability of the Fourier phase spectrum at JMA

Instead of using the site-effect substitution method, the author tested the similarity of Fourier phase characteristics between KUMA and JMA for several events.

Figure 13 shows the results for the event No.161. The Fourier phase characteristics at KUMA and JMA resemble each other at least for the horizontal components, although there is a notable difference for the vertical component. The author found that the degree of similarity depends on the event. However, the author acknowledges the importance of the event No.161 because the event presumably conveys similar path and site effects with the foreshock (Fig. 12) and the mainshock (Fig. 10). If the Fourier phase characteristics are similar for the event No.161 between KUMA and JMA, it could be reasonable to assume that they are similar also for the foreshock and the mainshock. Therefore, to estimate ground motions at KUMA during the foreshock and the mainshock under linear site response, the author simply used the Fourier phase characteristics at JMA for the same events.

Fourier amplitude spectrum

The Fourier amplitude spectra at KUMA during the foreshock and the mainshock were estimated in a similar way as in Step 2, that is, the Fourier amplitude spectra observed at JMA during the same events were multiplied by the spectral ratio shown in the top right panel of Fig. 3 for the horizontal components and the right panel of Fig. 4 for the vertical component. The author used the averaged ratios for the five events. To make sure that it is appropriate to use the averaged ratios for the five events, which did not necessarily occur near the target events, the author compared the averaged ratios for the five events with the spectral ratios for the event No.161, which presumably conveyed similar path and site effects with the target events as mentioned above. The results indicated that there is no significant difference between the spectral ratios at least for the horizontal components. Therefore, the author used the averaged ratios for the five events.

Incident (2E) ground motions at GL -31 m assuming linear multiple reflections

Next, the incident (2E) ground motions at GL-31m were estimated assuming linear multiple reflections. The soil layers were divided as shown in Table 2 according to the PS logging results provided by the organizer of the blind prediction (Matsushima et al., 2022). For the horizontal components, the S-wave velocities were used. For the vertical components, the P-wave velocities were used. For the 2nd, 4th, 5th, 6th and 7th layers, laboratory test results were available. The corresponding samples were T-1, T-2, Tr-3, Tr-4 and Tr-5, respectively. The author used the wet densities from the laboratory tests for these layers. For the 3rd, 8th and 9th layers, the author decided to use the same wet densities as the 6th, 6th and 5th layers, respectively, considering the similarity of the soils and N values. For the 1st layer and the “bedrock”, the author assumed a density of 2.0 g/cm³. The author used the damping coefficient of 0.02 for all the layers for all the analyses, referring to the results of cyclic triaxial tests.

Surface ground motions with effective stress analyses

Finally effective stress analyses were conducted to estimate surface ground motions under nonlinear site response. For this purpose, the author used a program called “FLIP” (Iai, 1991; Iai et al., 1992), which has been extensively used in the design of port structures in Japan. The parameters were determined based on a simplified procedure (Morita et al., 1997) from the PS logging results, N values and fine particle content provided by the organizer (Matsushima et al., 2022).

Table 3 shows the parameters used for the effective stress analyses. In this program, the shear modulus and the bulk modulus for the soil skeleton at small strain depend on some power of the effective confining pressure. They are specified for a reference effective confining pressure \({\sigma }_{m0}^{{\prime }}\). In this analysis, the effective confining pressure at the center of each layer was selected as the reference effective confining pressure. Then, the shear modulus at small strain \({G}_{m0}\) for the reference effective confining pressure was determined from the PS logging results. The bulk modulus for the soil skeleton \({K}_{m0}\) for the reference effective confining pressure was determined assuming a Poisson’s ratio of 1/3. Then they were assumed to be proportional to the square root of the effective confining pressure, which is a standard assumption in this program. For the sand and sandy silt (the 2nd, 3rd, 5th, 6th, 8th and 9th layers), \({\varphi }_{f}\) was determined from the N values based on Morita et al. (1997). For the silt and clay (the 4th and 7th layers), a shear strength equivalent to \({\varphi }_{f}\)=30° was used. For the porosity \(n\) and the maximum damping coefficient \({h}_{max}\), the values ordinarily used for FLIP analyses were used.

For the hatched layers in Table 3, the effects of excess pore pressure were considered. The parameters that control the excess pore pressure were determined from the N values and fine particle content based on Morita et al. (1997). The fine particle content was based on the laboratory test results for the 5th and 6th layers. For the 3rd, 8th and 9th layers, the author decided to use the same fine particle content as the 6th, 6th and 5th layers, respectively, considering the similarity of the soils and N values.

The input ground motions (NS and UD components in one case and EW and UD components in another case) were applied via a viscous boundary. In terms of the UD component, the result for the former case was selected to be submitted. The difference for the two cases was small.

In Fig. 14, the acceleration waveforms estimated and submitted by the author for the blind prediction Step 3 for the foreshock are compared with the recorded acceleration waveforms, which were distributed by the organizer after the blind prediction. The main phases in the recorded waveforms are captured by the estimated waveforms. However, for the latter part of the horizontal waveforms, the amplitudes are underestimated. In Fig. 15, the estimated velocity waveforms for the foreshock are compared with the velocity waveforms calculated from the records. The amplitudes are overestimated. In addition, there is a clear delay in the phase around 26 s in the NS component. In Fig. 16, the estimated Fourier spectra for the foreshock are compared with the Fourier spectra calculated from the records. For the horizontal components, the shift of the energy to lower frequency components in the estimated Fourier spectra due to soil nonlinearity is causing a discrepancy between the estimated and observed Fourier spectra.

The results for the mainshock (Figs. 17–19) showed a similar tendency. In the acceleration waveforms, the amplitudes were underestimated especially for the NS component. In the velocity waveforms, the amplitudes were overestimated especially for the NS component. In the Fourier spectra, the shift of the energy to lower frequency components in the estimated Fourier spectra due to soil nonlinearity is causing a discrepancy between the estimated and observed Fourier spectra especially for the NS component. It could be pointed out that the simulation result for the EW component for the mainshock is good in terms of velocity waveforms and Fourier spectra.

The simulation results described above for Step 3 such as the underestimation of accelerations, overestimation of velocities and the shift of the energy to lower frequencies may be indicating that the strong soil nonlinearity considered in the effective stress analyses is the main cause of the discrepancy between the estimated and observed ground motions. Therefore, in the following, the estimated ground motions under the assumption of linear site response at KUMA, which were the intermediate product of the blind prediction, will be compared with the recorded ground motions.

In Fig. 20, the acceleration waveforms estimated for the foreshock under linear site response are compared with the recorded acceleration waveforms. In contrast to the results in Fig. 14, there was no underestimation in the amplitude of the acceleration waveforms. In Fig. 21, the velocity waveforms estimated for the foreshock under linear site response are compared with the velocity waveforms calculated from the records. There was no delay in the phase around 26 s in the NS component, which was evident in the estimated waveform with the effective stress analysis (Fig. 15). In Fig. 22, the Fourier spectra estimated for the foreshock under linear site response are compared with the Fourier spectra calculated from the records. In contrast to the results in Fig. 16, the shift of the energy to lower frequency components for the horizontal components was not evident and the estimated Fourier spectra were much closer to the observed Fourier spectra. The results for the mainshock under linear site response (Figs. 23–25) showed a similar tendency. In the acceleration waveforms (Fig. 23), there was no underestimation in the amplitude in contrast to the results shown in Fig. 17. In the Fourier spectra (Fig. 25), the shift of the energy to lower frequency components for the horizontal components was not evident and the estimated Fourier spectra were much closer to the observed Fourier spectra compared to Fig. 19. One explanation for this result could be that the nonlinear soil behavior at the target site KUMA during the foreshock and the mainshock of the 2016 Kumamoto earthquake sequence was not a strong one but rather a weak one that involved slight increase in the damping factor without a significant decrease in the soil stiffness. Another explanation for this result could be that the effect of soil nonlinearity was already included in the records at JMA, which was neglected in the procedure the author followed. Therefore, the effect of soil nonlinearity was double counted in the results submitted by the author.

In this article, the methods employed by the author and the results for the ESG6 Blind Prediction Steps 2&3 were described. The target event for Step 2 was the April 16, 2016, 3:03 (JST), M5.9 earthquake that occurred in the Aso region. To simulate ground motions at the target site KUMA during this earthquake, the author used the site-effect substitution method. The Fourier amplitude spectrum was estimated from the spectral ratio between KUMA and a nearby station JMA. The Fourier phase spectrum was approximated by the spectrum at KUMA for another event that occurred in the Aso region. Comparison between the estimated and recorded ground motions after the blind prediction revealed that, except for the error in the arrival time, the estimated ground motions were fairly consistent with the observed ground motions. The result indicates the effectiveness of the site-effect substitution method when the rupture process of the target event is simple and the soil nonlinearity at the target site is not significant. The target events for Step 3 were the M6.5 foreshock and the M7.3 mainshock of the 2016 Kumamoto earthquake sequence. To simulate ground motions at KUMA during these earthquakes, the author used a similar method to estimate the Fourier amplitude spectrum, however, the author simply used the Fourier phase spectrum at JMA for the target events. Then, the author conducted effective stress analyses using a program called “FLIP”. The parameters were determined based on a simplified procedure from the PS logging results, N values and fine particle content. Comparison between the estimated and recorded ground motions after the blind prediction indicated that, although the estimated ground motions captured the main phases of the recorded ground motions, the low frequency components were overestimated and the high frequency components were underestimated in the horizontal components. The strong soil nonlinearity considered in the effective stress analyses was the main cause of the discrepancy between the estimated and observed ground motions. One explanation for this result could be that the nonlinear soil behavior at KUMA during the foreshock and the mainshock was not a strong one without a significant decrease in the soil stiffness. Another explanation could be that the effect of soil nonlinearity was already included in the records at JMA and the effect of soil nonlinearity was double counted in the results submitted by the author.

JMA: Japan Meteorological Agency

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The strong motion data at the station abbreviated as “JMA” in this article can be purchased from the Japan Meteorological Business Support Center.

Competing interests

Not applicable.

Funding

Not applicable.

Authors' contributions

A.N. did all the analysis and wrote the manuscript.

Acknowledgements

The author would like to thank for the ground motion data published by JMA at the station abbreviated as “JMA” in this article.

Asano K, Iwata T (2016) Source rupture processes of the foreshock and mainshock in the 2016 Kumamoto earthquake sequence estimated from the kinematic waveform inversion of strong motion data. Earth Planets and Space 68:147. doi:10.1186/s40623-016-0519-9
Chimoto K, Yamanaka H, Tsuno S, Matsushima S (2022) Predicted results of the velocity structure at the target site of the blind prediction exercise from microtremors and surface wave method as Step-1, -Report of the experiments for "The 6th International Symposium on Effects of Surface Geology on Seismic Motion". Earth Planets and Space, submitted.
Hata Y, Nozu A, Ichii K (2011) A practical method to estimate strong ground motions after an earthquake, based on site amplification and phase characteristics. Bulletin of the Seismological Society of America 101: 688-700.
Iai S (1991) A strain space multiple mechanism model for cyclic behavior of sand and its application. Earthquake Engineering Research Note 43. https://www.pari.go.jp/bsh/jbn-kzo/jbn-bsi/taisin/related/rela002.pdf. Accessed 27 Sep 2022
Iai S, Matsunaga Y, Kameoka T (1992) Strain space plasticity model for cyclic mobility. Soils and Foundations 32: 1-15.
Matsushima S, Yamanaka H, Tsuno S, Chimoto K, Suzuki H, Kawase H (2022) Investigation of the subsurface structure at the target site in Kumamoto, Japan and the distributed data of the blind prediction exercise, -Report of the experiments for "The 6th International Symposium on Effects of Surface Geology on Seismic Motion". Earth Planets and Space, submitted.
Morita T, Iai S, Liu H, Ichii K, Sato Y (1997) Simplified method to determine parameter of FLIP. Technical Note of the Port and Harbour Research Institute 869. https://www.pari.go.jp/search-pdf/no0869.pdf. Accessed 27 Sep 2022
Nozu A, Nagasaka Y (2017) Rupture process of the main shock of the 2016 Kumamoto earthquake with special reference to damaging ground motions: waveform inversion with empirical Green’s functions. Earth Planets and Space 69:22. doi:10.1186/s40623-017-0609-3
Tsuno S, Nagashima F, Kawase H, Yamanaka H, Matsushima S (2022) Predicted results of the weak and strong ground motions at the target site of the blind prediction exercise as Step-2 and Step-3, -Report of the experiments for "The 6th International Symposium on Effects of Surface Geology on Seismic Motion". Earth Planets and Space, submitted.

GraphicalAbstract.png
Table1.pdf
Table 1; Parameters of the earthquakes; The parameters are shown for the earthquakes for which ground motion data at KUMA were provided by the organizer and the M5.9 target event for Step 2. The numbers correspond to those in Figure 1. No.35 is the target event for Step 2.
Table2.pdf
Table 2; Parameters used for the deconvolution analyses to estimate incident ground motions.
Table3.pdf
Table 3; Parameters used for the effective stress analyses.

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Use of Fourier Phase Characteristics and Effective Stress Analyses: A Result for the ESG6 Blind Prediction Steps 2&3

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Abstract

Figures

Introduction

Step 2 – Simulation Of Weak Motions

Method

Fourier amplitude spectrum

Fourier phase spectrum

Results

Step 3 – Simulation Of Strong Motions

Applicability of the site-effect substitution method

Availability of the Fourier phase spectrum at JMA

Fourier amplitude spectrum

Incident (2E) ground motions at GL -31 m assuming linear multiple reflections

Surface ground motions with effective stress analyses

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Supplementary Files

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