Application of Probabilistic Fault Displacement Hazard Analysis in Japan

Takao, Makoto; Tsuchiyama, Jiro; Annaka, Tadashi; Kurita, Tetsushi

doi:10.5610/jaee.13.17

Cited by 26 publications

(22 citation statements)

References 10 publications

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“…Although focal depth and faulting type remain important parameters, they do not significantly affect the overall log-linear relationship of the regressed variables (Livio et al, 2016). (Youngs et al, 2003), (b) strike-slip faults (Petersen et al, 2011), and (c) reverse and strike-slip faults in Japan (Takao et al, 2013). …”

Section: Shortcomings With the Database For Probabilistic Fault Displmentioning

confidence: 98%

“…In Figs. 2.2 and 2.3, the currently available data regarding slip on DF and the conditional probability of slip are respectively given as a function of distance from the primary fault (e.g., Youngs et al, 2003;Petersen et al, 2011;Takao et al, 2013). The dataset of Petersen et al (2011) is derived from observations on steeply dipping, strike-slip faults of Mw 6.5-7.6, whereas the distribution considered by Youngs et al (2003) is developed from a dataset mainly composed of normal faults.…”

Section: Shortcomings With the Database For Probabilistic Fault Displmentioning

confidence: 99%

“…The dataset of Petersen et al (2011) is derived from observations on steeply dipping, strike-slip faults of Mw 6.5-7.6, whereas the distribution considered by Youngs et al (2003) is developed from a dataset mainly composed of normal faults. Takao et al (2013) analyzed data from reverse and strike-slip faults in Japan. Petersen et al (2011) conclude that the hazard for off-fault ruptures is much lower than the hazard near the fault.…”

Section: Shortcomings With the Database For Probabilistic Fault Displmentioning

confidence: 99%

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Earthquake-induced crustal deformation and consequences for fault displacement hazard analysis of nuclear power plants

Gürpinar¹,

Serva²,

Livio

et al. 2017

Nuclear Engineering and Design

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Readily available interferometric data (InSAR) of the coseismic deformation field caused by recent seismic\ud events clearly show that major earthquakes produce crustal deformation over wide areas, possibly resulting\ud in significant stress loading/unloading of the crust. Such stress must be considered in the evaluation\ud of seismic hazards of nuclear power plants (NPP) and, in particular, for the potential of surface slip (i.e.,\ud probabilistic fault displacement hazard analysis - PFDHA) on both primary and distributed faults.\ud In this study, based on the assumption that slip on pre-existing structures can represent the elastic\ud response of compliant fault zones to the permanent co-seismic stress changes induced by other major\ud seismogenic structures, we propose a three-step procedure to address fault displacement issues and consider\ud possible influence of surface faulting/deformation on vibratory ground motion (VGM). This\ud approach includes: (a) data on the presence and characteristics of capable faults, (b) data on recognized\ud and/or modeled co-seismic deformation fields and, where possible, (c) static stress transfer between\ud source and receiving faults of unknown capability.\ud The initial step involves the recognition of the major seismogenic structures nearest to the site and\ud their characterization in terms of maximum expected earthquake and the time frame to be considered\ud for determining their ‘‘capability” (as defined in the International Atomic Energy Agency - IAEA\ud Specific Safety Guide SSG-9). Then a GIS-based buffer approach is applied to identify all the faults near\ud the NPP, possibly influenced by the crustal deformation induced by the major seismogenic structures.\ud Faults inside these areas have to be tested for ‘‘capability” according to the same time window defined\ud for the primary seismogenic structures.\ud If fault capability is confirmed or, eventually, cannot be assessed, the next step is to implement an\ud approach based on the potential to affect the safety of the NPP site in terms of fault geometry, and potential\ud displacement.\ud Finally, in the case where the fault can affect the safety of the site, the third step is the PFDHA or, in\ud other words, the calculation of the annual probability of exceedance of the potential co-seismic fault displacement;\ud this displacement is to be compared with the fault displacement threshold that will impact\ud the safety of the NPP site.\ud We also consider the effect of site vicinity tectonism on site vibratory ground motion and discuss an\ud example in the light of the use of the GMPE

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Section: Shortcomings With the Database For Probabilistic Fault Displmentioning

confidence: 98%

Section: Shortcomings With the Database For Probabilistic Fault Displmentioning

confidence: 99%

Section: Shortcomings With the Database For Probabilistic Fault Displmentioning

confidence: 99%

See 1 more Smart Citation

Earthquake-induced crustal deformation and consequences for fault displacement hazard analysis of nuclear power plants

Gürpinar¹,

Serva²,

Livio

et al. 2017

Nuclear Engineering and Design

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show abstract

“…The PFDHA methodology was developed for normal faulting environments by the working group of Youngs et al (2003) and developed further for strike-slip faults by Petersen et al (2011). For the case of reverse faults, Moss and Ross (2011) worked specifically on PF data whereas Takao et al (2013) worked on both strike-slip and reverse faults but using data only from Japanese earthquakes. In depth analysis of the DR data for reverse faulting earthquakes has not received much attention from the scientific community.…”

Section: Introductionmentioning

confidence: 99%

Probability of Occurrence and Displacement Regression of Distributed Surface Rupturing for Reverse Earthquakes

et al. 2020

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“…This method computes the probability of faulting occurrence as a function of distance from the primary fault and of earthquake magnitude. Empirical regressions have been proposed for different tectonic styles (e.g., Youngs et al, , for normal faults; Petersen et al, , for strike‐slip faults; and Takao et al, , for reverse and strike‐slip faults) and are currently adopted in PFDHA. Nevertheless, following the observations and surveys performed during recent strong earthquakes, some shortcomings are emerging from these models, suggesting that the spatial density distribution of DF can be considerably influenced by deterministic factors, including the fault architecture at depth, the cut lithologies at surface, and the overburden thickness (e.g., Milliner et al, ; Teran et al, ; Zinke et al, ).…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Distributed Faulting During the 30 October 2016, Central Italy Earthquake: A Reference for Fault Displacement Hazard Assessment

Ferrario

Livio

2018

Tectonics

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Moderate to strong earthquakes (i.e., Mw >~6.0) commonly produce a complex network of ground ruptures, which are responsible for significant damage. Distributed faulting can affect wide areas (tenths of square kilometers), and expected displacement can be estimated through a probabilistic approach, considering distance from the primary fault and earthquake magnitude. Other factors may have a role in driving the occurrence of distributed faulting; nevertheless, they are not adequately addressed in the current modeling, due to a sensible lack of information. We study the 30 October 2016, Central Italy earthquake (Mw 6.5), to analyze the spatial pattern and geometric characteristics of distributed faulting. We found that distance from the primary structure, fault geometry, and lithology are key factors controlling the distributed faulting occurrence; the local structural setting (i.e., synthetic versus antithetic normal faults systems and relay zones) drives the spatial distribution of faults and the partitioning of the deformation. We also examine other four events occurred in the Italian Apennines since 1980, confirming that traditional models can underestimate the probability of distributed faulting. We suggest that a purely distance-based probabilistic approach should be integrated using additional parameters derived from earthquake deformation fields or considering the reactivation of preexisting faults.Plain Language Summary Surface faulting caused by moderate to strong earthquakes may occur along the major fault plane (primary fault) or along other structures in its proximity (distributed faulting). Distributed faulting can affect wide areas (tenths of square kilometers) and is responsible for significant damage. We assess the spatial pattern and geometric characteristics of distributed faulting of the 30 October 2016 Central Italy earthquake. We found that distance from the primary structure, fault geometry, and lithology are key factors controlling the distributed faulting occurrence. We also analyze four events that hit the Italian Apennines since 1980 and compare the results with the models currently adopted for estimating the possible amount of surface faulting caused by earthquakes. We found that current models, which are based on traditional field mapping methods, underestimate the probability of distributed faulting. We argue that the Central Italy 2016 event can act as a reference case study for fault displacement hazard assessment and that an increasing number of case histories will improve future mitigation strategies.PFDHA is the suggested approach for locating and designing critical or distributive infrastructures and utility lines, such as nuclear power plants (ANSI/ANS-2.30, 2015) and water pipelines. This method computes the probability of faulting occurrence as a function of distance from the primary fault and of earthquake FERRARIO AND LIVIO 1256

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Application of Probabilistic Fault Displacement Hazard Analysis in Japan

Cited by 26 publications

References 10 publications

Earthquake-induced crustal deformation and consequences for fault displacement hazard analysis of nuclear power plants

Earthquake-induced crustal deformation and consequences for fault displacement hazard analysis of nuclear power plants

Probability of Occurrence and Displacement Regression of Distributed Surface Rupturing for Reverse Earthquakes

Characterizing the Distributed Faulting During the 30 October 2016, Central Italy Earthquake: A Reference for Fault Displacement Hazard Assessment

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