This article describes an open-source site database for a total number of 1742 earthquake recording sites in the K-NET (Kyoshin network) and KiK-net (Kiban Kyoshin network) networks in Japan. This database contains site characterization parameters directly derived from available velocity profiles, including average wave velocities, bedrock depths, and velocity contrast. Meanwhile, it also consists of earthquake horizontal-to-vertical spectral ratio (HVSR) and peak parameters, for example, peak frequency, amplitude, width, and prominence. In addition, the site database also comprises topographic and geological proxies inferred from regional models or maps. Each parameter is derived in a consistent manner for all sites. This site database can benefit the application of machine learning techniques in studies on site amplification. Besides, it can facilitate, for instances, the search of the optimal site parameter(s) for the prediction of site amplification, the development and testing of ground-motion models or methodologies, as well as investigations on spatial or regional variability in site response. All resources (the site database, earthquake HVSR data at all sites, and the MATLAB script for peak identification) can be freely accessed via: https://doi.org/10.5880/GFZ.2.1.2020.006
AhdiSKAnchetaTDContrerasVKishidaTKwakDYKwokAOLParkerGARuzFandStewartJP (2020) Chapter 5: Site condition parameters. In: StewartJP (ed.) Data Resources for NGA-Subduction Project, PEER Report No. /202002. Berkeley, CA: Pacific Earthquake Engineering Research Center, University of California, Berkeley. Available at: https://cdn.ymaws.com/www.nibs.org/resource/resmgr/BSSC/P-749_Chapter5.pdf (accessed 30 December 2020).
2.
AllenTIWaldDJ (2009) On the use of high-resolution topographic data as a proxy for seismic site conditions (VS30). Bulletin of the Seismological Society of America99: 935–943.
3.
AoiSKunugiTNakamuraHFujiwaraH (2011) Deployment of new strong motion seismographs of K-NET and KiK-net. In: AkkarSGülkanPvan EckT (eds) Earthquake Data in Engineering Seismology: Geotechnical, Geological, and Earthquake Engineering, vol. 14. Dordrecht: Springer, pp. 167–186.
BalminoGValesNBonvalotSBriaisA (2012) Spherical harmonic modeling to ultra-high degree of Bouguer and isostatic anomalies. Journal of Geodesy86: 499–520.
6.
BergenKJJohnsonPAde HoopMVBerozaGC (2019) Machine learning for data-driven discovery in solid Earth geoscience. Science363: eaau0323.
7.
Bonnefoy-ClaudetSCottonFBardP-Y (2006) The nature of noise wavefield and its applications for site effects studies: A literature review. Earth-Science Reviews79: 205–227.
8.
BooreDMThompsonEMCadetH (2011) Regional correlations of VS30 and velocities averaged over depths less than and greater than 30 meters. Bulletin of the Seismological Society of America101: 3046–3059.
9.
BorcherdtRD (1994) Estimates of site-dependent response spectra for design (methodology and justification). Earthquake Spectra10: 617–654.
10.
BozorgniaYCampbellKW (2016) Vertical ground motion model for PGA, PGV, and linear response spectra using the NGA-West2 database. Earthquake Spectra32: 979–1004.
11.
CabasARodriguez-MarekABonillaLF (2017) Estimation of site specific kappa (κ0)-consistent damping values at KiK-net sites to assess the discrepancy between laboratory-based damping models and observed attenuation (of seismic waves) in the field. Bulletin of the Seismological Society of America107: 2258–2271.
12.
CampbellKWBozorgniaY (2014) NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5%-damped linear response spectra. Earthquake Spectra30: 1087–1115.
13.
CampbellKWBozorgniaY (2019) Ground motion models for the horizontal components of arias intensity (AI) and cumulative absolute velocity (CAV) using the NGA-West2 database. Earthquake Spectra35: 1289–1310.
14.
CastellaroSMulargiaFRossiPL (2008) Vs30: Proxy for seismic amplification?Seismological Research Letters79: 540–543.
15.
CottonFPousseGBonillaFScherbaumF (2008) On the discrepancy of recent European ground-motion observations and predictions from empirical models: Analysis of KiK-net accelerometric data and point-sources stochastic simulations. Bulletin of the Seismological Society of America98: 2244–2261.
16.
DartnellP (2000) Applying remote sensing techniques to map seafloor geology/habitat relationships. Master’s Thesis, San Francisco State University, San Francisco, CA.
17.
DobryROweisIUrzuaA (1976) Simplified procedures for estimating the fundamental period of a soil profile. Bulletin of the Seismological Society of America66(4): 1293–1321.
18.
European Standard EN 1998-1:2004 (2004) Eurocode 8: Design of structures for earthquake resistance —Part 1: General rules, seismic actions and rules for buildings.
19.
FisherRA (1958) Statistical Methods for Research Workers. 13th ed. New York: Hafner.
20.
FujimotoKMidorikawaS (2006) Relationship between average shear-wave velocity and site amplification inferred from strong motion records at nearby station pairs. Journal of Japan Association for Earthquake Engineering6: 11–22.
21.
FujiwaraHKawaiSAoiSMorikawaNSennaSAzumaHOoiMHaoKXHasegawaNIwakiAMaedaTWakamatsuKImotoMOkumuraTMatsuyamaHNaritaA (2012) Some improvements of seismic hazard assessment based on the 2011 Tohoku earthquake. Technical note of the National Research Institute for Earth Science and Disaster Prevention, number 379. Tsukuba, Japan: National Research Institute for Earth Science and Disaster Prevention.
GhofraniH (2012) An investigation into earthquake ground motion characteristics in Japan with emphasis on the 2011 M9.0 Tohoku earthquake. PhD Thesis, Western University, London, ON, Canada. Available at: https://ir.lib.uwo.ca/etd/887 (accessed 27 February 2019).
25.
GhofraniHAtkinsonGM (2014) Site condition evaluation using horizontal-to-vertical response spectral ratios of earthquakes in the NGA-West2 and Japanese databases. Soil Dynamics and Earthquake Engineering67: 30–43.
26.
HaghshenasEBardPYTheodulidisNSESAME WP04 Team (2008) Empirical evaluation of microtremor H/V spectral ratio. Bulletin of Earthquake Engineering6: 75–108.
27.
HashashYMIlhanAOHassaniBAtkinsonGMHarmonJShaoH (2020) Significance of site natural period effects for linear site amplification in central and eastern North America: Empirical and simulation-based models. Earthquake Spectra36: 87–110.
28.
HassaniBAtkinsonGM (2018) Application of a site-effects model based on peak frequency and average shear-wave velocity to California. Bulletin of the Seismological Society of America108: 351–357.
29.
HornBKP (1981) Hill shading and the reflectance map. Proceedings of the Institute of Electrical and Electronics Engineers69: 14–47.
30.
JiKWenRRenYDhakalYP (2019) Nonlinear seismic site response classification using K-means clustering algorithm: Case study of the September 6, 2018 Mw6.6 Hokkaido Iburi-Tobu earthquake, Japan. Soil Dynamics and Earthquake Engineering128: 105907.
31.
KaklamanosJBradleyBA (2018) Challenges in predicting seismic site response with 1D analyses: Conclusions from 114 KiK-net vertical seismometer arrays. Bulletin of the Seismological Society of America108: 2816–2838.
32.
KawaseHNagashimaFNakanoKMoriY (2019) Direct evaluation of S-wave amplification factors from microtremor H/V ratios: Double empirical corrections to “Nakamura” method. Soil Dynamics and Earthquake Engineering126: 105067.
33.
KawaseHSánchez-SesmaFJMatsushimaS (2011) The optimal use of horizontal-to-vertical (H/V) spectral ratios of earthquake motions for velocity structure inversions based on diffuse field theory for plane waves. Bulletin of the Seismological Society of America101(5): 2001–2014.
34.
KimSHwangYSeoHKimB (2020) Ground motion amplification models for Japan using machine learning techniques. Soil Dynamics and Earthquake Engineering132: 106095.
35.
KonnoKOhmachiT (1998) Ground-motion characteristics estimated from spectral ratio between horizontal and vertical components of microtremor. Bulletin of the Seismological Society of America88: 228–241.
36.
KothaSRCottonFBindiD (2018) A new approach to site classification: Mixed-effects ground motion prediction equation with spectral clustering of site amplification functions. Soil Dynamics and Earthquake Engineering110: 318–329.
37.
KwakDYSeyhanE (2020) Two-stage nonlinear site amplification modeling for Japan with VS30 and fundamental frequency dependency. Earthquake Spectra36(3): 1359–1385.
38.
KwakDYStewartJPMandokhailSJParkD (2017) Supplementing VS30 with H/V spectral ratios for predicting site effects. Bulletin of the Seismological Society of America107: 2028–2042.
39.
MarthewsTRDadsonSJLehnerBAbeleSGedneyN (2015) High-resolution global topographic index values for use in large-scale hydrological modelling. Hydrology and Earth System Sciences19: 91–104.
40.
NakamuraY (2019) What is the Nakamura method?Seismological Research Letters90: 1437–1443.
41.
NakanoKMatsushimaSKawaseH (2015) Statistical properties of strong ground motions from the generalized spectral inversion of data observed by K-NET, KiK-net, and the JMA Shindokei network in Japan. Bulletin of the Seismological Society of America105: 2662–2680.
OkadaYKasaharaKHoriSObaraKSekiguchiSFujiwaraHYamamotoA (2004) Recent progress of seismic observation networks in Japan-Hi-net, F-net, K-NET and KiK-net. Earth Planets Space56: xv–xxviii.
44.
PelletierJDBroxtonPDHazenbergPZengXTrochPANiuGYWilliamsZBrunkeMAGochisD (2016) A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. Journal of Advances in Modeling Earth Systems8: 41–65.
45.
PilzMCottonF (2019) Does the 1D assumption hold for site response analysis? A study of seismic site responses and implication for ground motion assessment using KiK-net strong-motion data. Earthquake Spectra2: 883–905.
46.
PitilakisKRigaEAnastasiadisA (2013) New code site classification, amplification factors and normalized response spectra based on a worldwide ground-motion database. Bulletin of Earthquake Engineering11: 925–966.
47.
RenYFWenRZYaoXXJiK (2017) Five parameters for the evaluation of the soil nonlinearity during the Ms8.0 Wenchuan Earthquake using the HVSR method. Earth Planets Space69: 116.
48.
RileySJDeGloriaSDElliotR (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences5: 23–27.
49.
Sánchez-SesmaFJRodríguezMIturrarán-ViverosULuzónFCampilloMMargerinLGarcía-JerezASuarezMSantoyoMARodríguez-CastellanosA (2011) A theory for microtremor H/V spectral ratio: Application for a layered medium. Geophysical Journal International186: 221–225.
50.
SchoberPBoerCSchwarteLA (2018) Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia126: 1763–1768.
51.
SDGM (2014) 1:20,000 Seamless Digital Geological Map of Japan (SDGM), Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology. Available at: https://gbank.gsj.jp/seamless/index_en.html? (accessed 30 December 2019).
52.
SeyhanEStewartJPAnchetaTDDarraghRBGravesRW (2014) NGA-West2 site database. Earthquake Spectra30: 1007–1024.
53.
WakamatsuKMatsuokaM (2013) Developing a 7.5-sec site-condition map for Japan based on geomorphologic classification. WIT Transactions on the Built Environment120: 101–112.
54.
WakamatsuKKuboSMatsuokaMHasegawaKSugiuraM (2005) Japan Engineering Geomorphologic Classification Map with CD-ROM Database. Tokyo, Japan: University of Tokyo Press.
55.
WaldDJAllenTI (2007) Topographic slope as a proxy for seismic site conditions and amplification. Bulletin of the Seismological Society of America97: 1379–1395.
56.
WeatherillGKothaSRCottonF (2020) Re-thinking site amplification in regional seismic risk assessment. Earthquake Spectra36: 274–297.
57.
WeissAD (2001) Topographic Positions and Landforms Analysis (poster), ESRI International User Conference, July 2001. San Diego, CA: ESRI.
58.
WillsCJClahanKB (2006) Developing a map of geologically defined site-condition categories for California. Bulletin of the Seismological Society of America96: 1483–1501.
59.
WilsonMFO’ConnellJBBrownCGuinanJCGrehenAJ (2007) Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Marine Geodesy30: 3–35.
60.
WooleroyEWStreetR (2002) 3D near-surface soil response from H/V ambient-noise ratios. Soil Dynamics and Earthquake Engineering22: 865–876.
61.
YongAHoughSEIwahashiJBravermanA (2012) A terrain-based site-conditions map of California with implications for the contiguous United States. Bulletin of the Seismological Society of America102: 114–128.
62.
ZevenbergenLWThorneCR (1987) Quantitative analysis of land surface topography. Earth Surface Processes and Landforms12: 47–56.
63.
ZhaoCZhaoJX (2019) S- and P-wave spectral ratios for on-site earthquake early warning in Japan. Bulletin of the Seismological Society of America109: 395–412.
64.
ZhuCCottonFPilzM (2019a) Testing the depths to 1.0 and 2.5 km/s velocity isosurfaces in a velocity model for Japan and implications for ground-motion modeling. Bulletin of the Seismological Society of America109: 2710–2721.
65.
ZhuCCottonFPilzM (2020a) Detecting site resonant frequency using HVSR: Fourier versus response spectrum and the first versus the highest peak frequency. Bulletin of the Seismological Society of America110: 427–440.
66.
ZhuCPilzMCottonF (2020b) Evaluation of a novel application of earthquake HVSR in site-specific amplification estimation. Soil Dynamics and Earthquake Engineering139: 106301.
67.
ZhuCPilzMCottonF (2020c) Which is a better proxy, site period or depth to bedrock, in modelling linear site response in addition to the average shear-wave velocity?Bulletin of the Seismological Society of America18: 797–820.
ZhuJDaleyMDBaiseLGThompsonEMWaldDJKnudsenKL (2019b) A geospatial liquefaction model for rapid response and loss estimation. Earthquake Spectra31: 1813–1837.
70.
ZuberMTSmithDEWatkinsMMAsmarSWKonoplivASLemoineFGJay MeloshHNeumannGAPhillipsRJSolomonSCWieczorekMAWilliamsJGGoossensSJKruizingaGMazaricoEParkRSYuanD-N (2012) Gravity field of the moon from the Gravity Recovery and Interior Laboratory (GRAIL) mission. Science339: 668–671.
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