On the Effect of Synthetic and Real Data Properties on Seismic Intensity Prediction Equations

被引:0
|
作者
Roman N. Vakarchuk
Päivi Mäntyniemi
Ruben E. Tatevossian
机构
[1] Russian Academy of Sciences,Institute of Physics of the Earth
[2] University of Helsinki,Department of Geosciences and Geography, Institute of Seismology
来源
关键词
Macroseismology; Intensity data point; Seismic intensity prediction equation; United Kingdom;
D O I
暂无
中图分类号
学科分类号
摘要
The present investigation focuses on the effect of input data properties on the estimation of seismic intensity prediction equation (IPE) coefficients. Emphasis is placed on small-to-moderate magnitude earthquakes. Synthetic intensity data points (IDPs) are created using a given IPE, assuming independence of azimuth. Extensive simulations are performed for single earthquakes and a synthetic database. Tests of single earthquakes show that increasing the sample size narrows the range of obtained coefficients. The larger the difference between the shortest and longest distance of IDPs from the epicentre, the narrower is this range. A short radius of perceptibility is more rapidly saturated with new data points than a long one. The synthetic database is used to examine the effect of magnitude and depth errors. The performance of synthetic data gives a model with which the real data can be compared. The attenuation coefficient appears stable against magnitude errors of ± 0.2 units, but starts to be overestimated as magnitude errors increase. Assuming an erroneous regional depth easily leads to intensity differences of 1 degree. The mean coefficient values deviate from the correct ones and tend to increase with depth. The results resemble the synthetic ones, but imply larger uncertainties. The attenuation coefficient, ν, appears to be the least sensitive coefficient to errors. Real data from seven post-1965 earthquakes in the magnitude range of 4.0–5.2 were retrieved from the intensity database of the United Kingdom.
引用
收藏
页码:4261 / 4275
页数:14
相关论文
共 50 条
  • [21] Real-time seismic intensity prediction using frequency-dependent site amplification factors
    Ogiso, Masashi
    Aoki, Shigeki
    Hoshiba, Mitsuyuki
    EARTH PLANETS AND SPACE, 2016, 68
  • [22] Real-time seismic intensity prediction using frequency-dependent site amplification factors
    Masashi Ogiso
    Shigeki Aoki
    Mitsuyuki Hoshiba
    Earth, Planets and Space, 68
  • [23] Real-Time Seismic Intensity Measurements Prediction for Earthquake Early Warning: A Systematic Literature Review
    Cheng, Zhenpeng
    Peng, Chaoyong
    Chen, Meirong
    SENSORS, 2023, 23 (11)
  • [24] Is this Real? Generating Synthetic Data that Looks Real
    Mannino, Miro
    Abouzied, Azza
    PROCEEDINGS OF THE 32ND ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY (UIST 2019), 2019, : 549 - 561
  • [25] Validating Intensity Prediction Equations for Italy by Observations
    Mak, Sum
    Clements, Robert Alan
    Schorlemmer, Danijel
    BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2015, 105 (06) : 2942 - 2954
  • [26] Application of kriging technique to seismic intensity data
    De Rubeis, V
    Tosi, P
    Gasparini, C
    Solipaca, A
    BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, 2005, 95 (02) : 540 - 548
  • [27] An ordered probit model for seismic intensity data
    Michela Cameletti
    Valerio De Rubeis
    Clarissa Ferrari
    Paola Sbarra
    Patrizia Tosi
    Stochastic Environmental Research and Risk Assessment, 2017, 31 : 1593 - 1602
  • [28] An ordered probit model for seismic intensity data
    Cameletti, Michela
    De Rubeis, Valerio
    Ferrari, Clarissa
    Sbarra, Paola
    Tosi, Patrizia
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2017, 31 (07) : 1593 - 1602
  • [29] Synthetic seismic data generation with deep learning
    Roncoroni, G.
    Fortini, C.
    Bortolussi, L.
    Bienati, N.
    Pipan, M.
    JOURNAL OF APPLIED GEOPHYSICS, 2021, 190 (190)
  • [30] Partial differential equations discovery with EPDE framework: Application for real and synthetic data [Formula presented]
    Maslyaev, Mikhail
    Hvatov, Alexander
    Kalyuzhnaya, Anna V.
    Journal of Computational Science, 2021, 53