Kumamoto Earthquake Research Articles

Stress conditions and seismicity around the rupture zone of the mainshock of the 2016 Kumamoto earthquake sequence in Kyushu, southwest Japan

The 2016 Kumamoto earthquake sequence in Kyushu included a foreshock (Mw 6.2) on 14 April and the mainshock (Mw 7.0) on 16 April, both of which were caused by fault ruptures at the intersection of the Futagawa and Hinagu fault zones. However, not all sections of the two fault zones were ruptured during the mainshock; in particular, although the northernmost (Takano–Shirahata) section of the Hinagu fault zone ruptured, the rupture did not propagate to southern sections of the fault zone. We examined fault geometry, geological structure, and seismicity around the fault zones, and conducted numerical Coulomb stress change and slip tendency analyses to investigate rupture conditions around the source faults. Fault geometry and slip tendencies indicated that before the foreshock, the source fault (the Futagawa section of the Futagawa fault zone) was favorable for rupture. Seismicity analysis showed that, before the foreshock, a remarkable low-seismicity zone existed where the rupture ultimately terminated. A material or structural boundary in the fault zones probably caused this anomalous seismicity and affected rupture propagation during the mainshock. Coulomb stress change analysis indicated a positive stress change in the Hinagu section just after the mainshock. In addition, the strikes of the optimum slip planes were parallel to that of the Hinagu section just after the mainshock, and slip tendencies in the section increased at that time. These results suggest that the Hinagu section was brought closer to failure just after the mainshock. In contrast, later in the post-mainshock period, the strikes of the optimum slip planes were oblique to that of the Hinagu section and the slip tendencies of the section were low. These results suggest that the favorability for a future rupture of the Hinagu section has declined since 2016 Kumamoto earthquake sequence.

Analysis of Measuring Precision of 3D Model of Ishigaki Stone in Kumamoto Castle Using Automatic CMG Crane System

Kumamoto Castle, consisting of towers and stone walls called Ishigaki, was severely damaged by the 2016 Kumamoto earthquake. As this castle has cultural significance in Japan, the damaged stone walls of the castle need to be promptly and accurately restored. In recent years, the utilization of 3D stone data has been studied to determine the position of collapsed stones precisely. The 3D stone data needs to be measured automatically in a short period of time for the efficient restoration of stone walls. To control the attitude of the stone, a small control moment gyro (CMG) device was proposed, and a robust controller with a model error compensator was applied to the CMG device. In this study, the measurement precision of a 3D model of stones using the CMG crane system was analyzed. The automatic measuring system consists of a CMG device and an elevator for a 3D scanner to reduce the load on workers. The scanning motion was planned automatically based on the shape and position of stones using depth sensors. The effectiveness of the proposed measuring system was evaluated using the accuracy and measurement time including the data correction time of the 3D modeling.

Simulation of soil liquefaction distribution in downtown Mashiki during 2016 Kumamoto earthquake using nonlinear site response

Several sites located between Road No.28 and Akitsu River in downtown Mashiki were liquefied during the mainshock of the 2016 Kumamoto earthquake. According to the building damage survey results, only a few buildings were damaged in areas proximate to the Akitsu River, where liquefaction occurred, however, serious building damage occurred in neighboring regions. Therefore, the effect of soil liquefaction on strong ground motions in Mashiki should be ascertained. Moreover, the distribution of visible and invisible liquefaction is required to be estimated as well. In this study, the distribution of depth of groundwater level in Mashiki was studied, which decreased from 14 to 0 m from northeast to southwest. Thereafter, the nonlinearities of the shallow layers at four borehole drilling sites were identified from the experimental data using the Ramberg–Osgood relationship. Subsequently, the dynamic nonlinear effective stress analysis of the one-dimensional soil column was performed to 592 sites in Mashiki between the seismological bedrock and ground surface to estimate the distribution of strong ground motions during the mainshock. First, the ground motions estimated by the nonlinear analysis corresponded to the ground motions observed at the Kik-net KMMH16. Second, the soil nonlinearity of shallow layers was considerably strong in the entire target area especially in the southern Mashiki, and the PGV distribution was similar to the building damage distribution after the mainshock. Furthermore, the estimated distribution of the soil liquefaction site was similar to the observed results, whereas certain invisible-liquefaction sites were estimated in the north and middle of the target area.

Comparison between the Hamiltonian Monte Carlo method and the Metropolis–Hastings method for coseismic fault model estimation

A rapid source fault estimation and quantitative assessment of the uncertainty of the estimated model can elucidate the occurrence mechanism of earthquakes and inform disaster damage mitigation. The Bayesian statistical method that addresses the posterior distribution of unknowns using the Markov chain Monte Carlo (MCMC) method is significant for uncertainty assessment. The Metropolis–Hastings method, especially the Random walk Metropolis–Hastings (RWMH), has many applications, including coseismic fault estimation. However, RWMH exhibits a trade-off between the transition distance and the acceptance ratio of parameter transition candidates and requires a long mixing time, particularly in solving high-dimensional problems. This necessitates a more efficient Bayesian method. In this study, we developed a fault estimation algorithm using the Hamiltonian Monte Carlo (HMC) method, which is considered more efficient than the other MCMC method, but its applicability has not been sufficiently validated to estimate the coseismic fault for the first time. HMC can conduct sampling more intelligently with the gradient information of the posterior distribution. We applied our algorithm to the 2016 Kumamoto earthquake (MJMA 7.3), and its sampling converged in 2 × 104 samples, including 1 × 103 burn-in samples. The estimated models satisfactorily accounted for the input data; the variance reduction was approximately 88%, and the estimated fault parameters and event magnitude were consistent with those reported in previous studies. HMC could acquire similar results using only 2% of the RWMH chains. Moreover, the power spectral density (PSD) of each model parameter's Markov chain showed this method exhibited a low correlation with the subsequent sample and a long transition distance between samples. These results indicate HMC has advantages in terms of chain length than RWMH, expecting a more efficient estimation for a high-dimensional problem that requires a long mixing time or a problem using nonlinear Green’s function, which has a large computational cost.Graphical

XYtracker: a new approach to estimate fault rupture extent in real time for large earthquakes

We present a methodology for estimating fault geometry and utilizing the distance to the fault for the shaking estimation to improve the accuracy of real-time shaking estimates for large earthquakes. Most of the earthquake early warning system currently estimates the seismic intensity with the ground-motion prediction equations (GMPE) as a function of the hypocenter distance. However, using the fault distance computed from a finite source model can improve the accuracy of the shaking intensity estimation for large earthquakes. This study proposes a novel methodology, XYtracker, to estimate the surface projection of the fault extent and real-time seismic intensity. For large earthquakes, high-frequency ground motions tend to saturate over the magnitude range and strongly correlate with fault distance. As a result, this work can achieve the fault extent using seismic intensity and GMPE. We considered three types of fault models: point-source, line-source, and rectangle-source model. We found the most probable model parameters for each model by minimizing the residual sum of squares between the observed and estimated seismic intensities. The Akaike Information Criterion selected the most probable model among them. The strong motion data set of the 2008 Wenchuan, 2011 Tohoku, and 2016 Kumamoto earthquakes was used to test our methodology. The new method for estimating the fault geometry can obtain the ongoing rupture length and direction using the strong motion data. The model selection scheme with the Akaike Information Criterion selected the finite-source model to explain the shaking distribution. Results revealed that this new approach performed well in estimating the fault dimension. The method can promote the accuracy of the seismic intensity estimation for future large earthquakes, including the subduction earthquakes.Graphical

Enhancement of direct waves based on the probability density function of seismic wave amplitudes

SUMMARY The enhancement of direct waves in seismic records is desirable in various seismological situations, where the accurate picking of seismic phases is necessary for hypocentre location, spectral inversion, waveform inversion and seismic tomography. In this study, we developed a new method to enhance direct waves in seismic waveforms based on the statistical distribution (probability density function, PDF) of the amplitudes of waveforms. The seismic waveform changes from a direct wave to a coda wave as time proceeds, which also causes the PDF of its amplitude to vary with time. The PDF becomes similar to a Gaussian or Rayleigh distribution with time, and hence, direct waves can be enhanced using the dissimilarity between the PDF of the amplitude of the seismic waveform and the Gaussian or Rayleigh distribution. In the proposed method, the Kullback–Leibler divergence is used to measure dissimilarity and it is applied as a weight. We confirmed that the method can effectively enhance direct P and S waves by applying it to seismic records of the 2016 Kumamoto earthquake, synthetic waveforms with noise and volcanic events. The method was also applied to waveforms of the 2008 Iwate–Miyagi Nairiku earthquake, which revealed that direct waves were enhanced, and an overview of the wave field and rupture process was obtained. The proposed method can detect direct waves even from subevents, thereby producing detailed information on the earthquake rupture process. We detected two significant pulses and determined their source location and rupture times. The estimated pulse sources were consistent with parts of the rupture scenario proposed by the previous studies. In addition, for local and teleseismic event records, the proposed method was compared with other methods to study its merits and demerits. The present method can enhance seismic phases on records of local and teleseismic events. Our method detects or enhances direct waves based neither on their amplitudes nor on their temporal variations but on the statistical distribution of their amplitudes, which has both advantages and disadvantages. Although polarization filtering is a powerful method that can enhance seismic phases, it requires three-component waveforms. The proposed method can be applied even to a single component record, which is a significant advantage.

The Origin of Large, Long‐Period Near‐Fault Ground Velocities During Surface‐Breaking Strike‐Slip Earthquakes

AbstractRecords of near‐fault ground motions from recent surface‐breaking earthquakes are characterized by large (> a few m/s), long‐period (a few seconds) ground velocity pulses, which may pose significant hazard for tall buildings and large infrastructures. Yet, the generation mechanism is not well understood. Here, using spontaneous rupture simulations, we examine the origin of large velocity pulses observed during the 2016 Mw7.0 Kumamoto (Japan) earthquake. We show that near‐fault waveform data as well as seismologically estimated moment and radiated energy can be well reproduced by a relatively simple model with uniform along‐strike pre‐stress and frictional properties. Our results suggest that large, long‐period ground velocities are caused by the dynamic interaction of propagating rupture and the Earth's surface, which is enhanced by reflected waves from the boundaries of low‐velocity layers. Such a generic mechanism suggests that large, long‐period ground motion is a common occurrence in near‐fault regions during surface‐breaking, strike‐slip earthquakes.

Hospital Evacuation Implications After the 2016 Kumamoto Earthquake.

During the 2016 Kumamoto earthquake, 10 hospitals took responsibility for complete evacuation, in what has become regarded as one of the largest evacuations of patients in 1 seismic disaster. We aimed to examine the reasons for evacuation and to assess hospital vulnerability as well as preparedness for the earthquake. A multidisciplinary team conducted semi-structured interviews with the hospitals 6 months after the earthquake. The primary reasons for the decision to evacuate hospitals were categorized into 3: 1) Concern for structural safety (4 facilities), 2) Damage to the facility water system (7 facilities), and 3) Cessation of regional water supply (5 facilities).All hospitals decided on immediate evacuation within 30 hours and could not wait for structural engineers to inspect the affected buildings. Damage to sprinklers or water facilities caused severe water shortages and flood, thus requiring weeks to resume inpatient care. The earthquake revealed the vulnerability of rapid building-inspection systems, aging buildings, and water infrastructure.

Depression, Insomnia, and Probable Post-Traumatic Stress Disorder among Survivors of the 2016 Kumamoto Earthquake and Related Factors during the Recovery Period Amidst the COVID-19 Pandemic.

The aftereffects of the severe 2016 Kumamoto earthquake were complicated by the COVID-19 pandemic. This study aimed to identify mental health problems and related factors among survivors five years after the earthquake and clarify its long-term effects. A cross-sectional survey was conducted in 2020 among 19,212 survivors affected by the earthquake who moved from temporary to permanent housing. We analysed 8966 respondents (5135 women, 3831 men; mean age 62.25 ± 17.29 years). Logistic regression analysis was conducted to examine associations between mental health problems and socioeconomic factors. Prevalence rates of psychological distress, insomnia, and probable post-traumatic stress disorder were 11.9%, 35.2%, and 4.1%, respectively. Female gender (OR = 1.33, 95% CI = 1.13–1.57; OR = 1.21, 95% CI = 1.08–1.34; OR = 1.81, 95% CI = 1.41–2.32), public housing (OR = 2.14, 95% CI = 1.63–2.83; OR = 1.54, 95% CI = 1.26–1.88; OR = 2.41, 95% CI = 1.62–3.58), loneliness (OR = 9.08, 95% CI = 7.71–10.70; OR = 5.55, 95% CI = 4.90–6.30; OR = 3.52, 95% CI = 2.77–4.49), COVID-19-induced activity reduction (OR = 1.41, 95% CI = 1.19–1.66; OR = 1.86, 95% CI = 1.68–2.07; OR = 1.80, 95% CI = 1.40–2.31), and COVID-19-induced income reduction (OR = 1.33, 95% CI = 1.12–1.57; OR = 1.43, 95% CI = 1.28–1.59; OR = 1.92, 95% CI = 1.51–2.43) were significantly associated with mental health problems. These results suggest that gender, current housing, loneliness, and COVID-19 affected the survivors’ mental health during recovery.

Development of Fragility Curves for Japanese Buildings Based on Integrated Damage Data from the 2016 Kumamoto Earthquake

A series of earthquakes hit Kumamoto Prefecture on Kyushu Island, Japan on April 14 and 16, 2016. Soon after the 2016 Kumamoto earthquake, municipal governments carried out building damage surveys to issue disaster-victim certificates. The building damage caused by the 2016 Kumamoto earthquake was serious in Mashiki Town, which is located close to the source region, but Uki City, next to that area, was also heavily damaged. The present authors have already developed several fragility curves using damage survey data from the Mashiki Town and Uki City governments. Some of the fragility curves for Mashiki Town rise sharply at low PGV levels because of the lack of damage data in the low PGV range. Compared with the fragility curves for Mashiki Town, the fragility curves for Uki City show a lower damage ratio for the same PGV. The fragility curves for Uki City were obtained using the damage data in the PGV range lower than those for Mashiki Town. In this study, we integrated the damage survey data of the Mashiki Town government and the Uki City government due to the 2016 Kumamoto earthquake, and constructed building fragility curves with respect to the structural material and construction period in combination with the estimated seismic motion distribution. The correlation coefficient showed a strong positive correlation of approximately 0.9 in all classifications of the building fragility curves covering a wide range of seismic ground motions. The validity of the proposed fragility curves was demonstrated and compared with existing fragility curves developed using damage survey data from local governments in other earthquakes.

Resilience of pregnant and postpartum women affected by the 2016 Kumamoto earthquake: A qualitative study

Resilience of pregnant and postpartum women affected by the 2016 Kumamoto earthquake: A qualitative study

Long-term changes in crustal movements and deformations before and during the 2016 Kumamoto earthquake sequence

The Kyushu Island, as well as whole Japanese archipelago, is equipped with dense GPS network (GEONET). It allows us to track the movements and deformations of the earth’s surface over long-term time intervals. In this study, based on daily determinations of the coordinates of GPS stations, analysis has been made on long-term trends in the accumulation of movements and deformations in large areas of the Kyushu Island before the series of April 14–16, 2016 Kumamoto earthquakes to identify deformation precursors and locked, immobile fault zones. The study of the seismic deformation process was performed using the data from 70 continuous permanent GPS stations for the period 2009–2016.The movement and deformation features found characterize the kinematics of the axial zone of the southwestern part of the island arc of the Japanese archipelago. The combination of coseismic compression and uplift in the center of the formed triad of deformation extrema and the consistency between subsidence and extensions at its edges demonstrate the mechanism of growth of the central region of the island arc under compression and the role of volcanism. Of particular interest in the context of the development of movements and deformations during the generation of the Kumamoto earthquakes is the behavior of the minimum displacement moduli of the GNSS sites. Analysis of their kinematics shows the formation of a zone of minimum displacements, in which subsequent strong seismic events were localized. It is shown that rather dense and extensive GNSS networks allow observing and studying the seismic-deformation process at the stages of seismic generation, discharge and relaxation, thereby providing an empirical basis for the development of models for predicting large seismic events.

Retraction Note to: Structural features and seismotectonic implications of coseismic surface ruptures produced by the 2016 Mw 7.1 Kumamoto earthquake

Retraction Note to: Structural features and seismotectonic implications of coseismic surface ruptures produced by the 2016 Mw 7.1 Kumamoto earthquake

Lessons learned from psychosocial support and mental health surveys during the 10 years since the Great East Japan Earthquake: Establishing evidence-based disaster psychiatry.

Post‐disaster mental health and psychosocial support have drawn attention in Japan after the 1995 Great Hanshin‐Awaji Earthquake, with mental health care centers for the affected communities being organized. After the catastrophe, a reconstruction budget was allocated to organize mental health care centers to provide psychosocial support for communities affected by the 2007 Chūetsu offshore earthquake, the 2011 Great East Japan Earthquake, and the 2016 Kumamoto Earthquake. There were several major improvements in post‐disaster mental health measures after the Great East Japan Earthquake. The Disaster Psychiatric Assistance Team system was organized after the earthquake to orchestrate disaster response related to the psychiatric health system and mental health of the affected communities. Special mental health care efforts were drawn to the communities affected by the nuclear power plant accident through Chemical, Biological, Radiological, Nuclear, and high yield Explosives, being succeeded by measures against the coronavirus pandemic. As another new movement after the Great East Japan Earthquake, the number of surveys involving communities affected by disasters has soared. More than 10 times the number of scientific publications were made in English during the decade following the Great East Japan Earthquake, compared with the previous decades. In this review, we examined the results and issues acquired in the 10 years since the Great East Japan Earthquake, proposing evidence‐based disaster psychiatry as the direction of future mental health measures related to emergency preparedness and response.

Predictive Simulation for Surface Fault Occurrence Using High-Performance Computing

Numerical simulations based on continuum mechanics are promising methods for the estimation of surface fault displacements. We developed a parallel finite element method program to perform such simulations and applied the program to reproduce the 2016 Kumamoto earthquake, where surface rupture was observed. We constructed an analysis model of the 5 × 5 × 1 km domain, including primary and secondary faults, and inputted the slip distribution of the primary fault, which was obtained through inversion analysis and the elastic theory of dislocation. The simulated slips on the surface were in good agreement with the observations. We then conducted a predictive simulation by inputting the slip distributions of the primary fault, which were determined using a strong ground motion prediction method for an earthquake with a specified source fault. In this simulation, no surface slip was induced in the sub-faults. A large surface slip area must be established near a sub-fault to induce the occurrence of a slip on the surface.

Damaged Building Extraction Using Modified Mask R-CNN Model Using Post-Event Aerial Images of the 2016 Kumamoto Earthquake

Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.

Policies of Disaster Management in Japan for Preparation for Nankai Megathrust Earthquake

If the Nankai Megathrust Earthquake should occur, it is clear that the disaster would become a national catastrophe. To avoid the decline of Japan caused by such earthquake first of all the proactive measures, improvement of prevention, and the reactive ones, strengthening of recovery, have to be taken by means of Disaster Resilience. Looking back on the history of Japan, we can easily understand that Japan is a lucky country, because it has never experienced the disturbances of war or huge disaster leading to a collapse of the country. However, it has become obvious that if the Nankai Megathrust Earthquake should occur, it would trigger the decline of Japan. Therefore, the Ministry of Disaster Management should be established to prevent a disaster as national catastrophe and collapse of the country. For this reason the main objectives and responses of the Ministry of Disaster Management are clarified in this paper. Especially in the phases of initial response and life rebuilding making use of the experiences of the 2016 Kumamoto Earthquake and recognizing that the Tokai Earthquake cannot be predicted, the issuance of the emergency information of the Nankai Megathrust Earthquake and its problems are discussed and summarized.

A new index representative of seismic cracks to assess post‐seismic landslide susceptibility

AbstractIn a major earthquake, strong shaking reduces the natural soil strength. Therefore, evaluation of the susceptibility of post‐seismic landslides may help prevent further disasters in an affected area. This study developed an index representing the spatial density of seismic cracks—the dense crack index (DCI)—to express the degree of local ground effects. An area of 6 km2 with 196 slides identified after the 2016 Kumamoto earthquake (Mw 7.0) was analyzed using models that incorporated the weight of evidence (WoE), logistic regression, and random forest (RF) methods, with the DCI as a conditioning factor. The WoE and RF models confirmed the importance of the DCI, although the improvement in performance as indicated by area under the curve values was marginal or negligible. This was largely due to combinations of other factors that enabled inclusion of locations with strong seismic waves, which compensated for the absence of the index. The contribution of the DCI could be improved if more accurate LiDAR data were used in the analysis.

Estimation of Groundwater Recharge in Kumamoto Area, Japan in 2016 by Mapping Land Cover Using GIS Data and SPOT 6/7 Satellite Images

Agricultural fields, grasslands, and forests are very important areas for groundwater recharge. However, these types of land cover in the Kumamoto area, Japan, were damaged by the Kumamoto earthquake and heavy rains in 2016. In this region, where groundwater provides almost 100% of the domestic water supply for a population of about 1 million, quantitative evaluation of changes in groundwater recharge due to land cover changes induced by natural disasters is important for the sustainable use of groundwater in the future. The objective of this study was to create a land cover map and estimate the groundwater recharge in 2016. Geographic information system (GIS) data and SPOT 6/7 satellite images were used to classify the Kumamoto area into nine categories. The maximum likelihood classifier of supervised classification was applied in ENVI 5.6. Eventually, the map was cleaned up with a 21 × 21 kernel filter, which is larger than the common size of 3 × 3. The created land cover map showed good performance of the larger filter size and sufficient validity, with overall accuracy of 91.7% and a kappa coefficient of 0.88. The estimated total groundwater recharge amount reached 757.56 million m3. However, if areas of paddy field, grassland, and forest had not been reduced due to the natural disasters, it is estimated that the total groundwater recharge amount would have been 759.86 million m3, meaning a decrease of 2.30 million m3 in total. The decrease of 2.13 million m3 in the paddy fields is temporary, because the paddy fields and irrigation channels have been improved and the recharge amount will recover. On the other hand, since the topsoil on the landslide scars will not recover easily in natural conditions, it is expected to take at least 100 years for the groundwater recharge to return to its original state. The recharge amount was estimated to decrease by 0.17 million m3 due to landslides. This amount is quite small compared to the total recharge amount. However, since the reduced recharge amount accounts for the annual water consumption for 1362 people, and 12.1% of the recharge decrease of 1.41 million m3 each year to fiscal year 2024 is expected by municipalities, we conclude that efforts should be made to compensate for the reduced amount due to the disasters.

Modelling of 2016 Kumamoto earthquake by integrating site effect in semi-empirical technique

Modelling of 2016 Kumamoto earthquake by integrating site effect in semi-empirical technique