A new method of seismic site classification using HVSR curves: A case study of the 12 November 2017 Mw 7.3 Ezgeleh earthquake in Iran

Saman Yaghmaei-Sabegh; Rajesh Rupakhety

doi:10.1016/j.enggeo.2020.105574

A new method of seismic site classification using HVSR curves: A case study of the 12 November 2017 Mw 7.3 Ezgeleh earthquake in Iran

Saman Yaghmaei-Sabegh^*, Rajesh Rupakhety

^*Corresponding author for this work

Engineering and Natural Sciences

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

On the 12th of November 2017, a strong earthquake with a moment magnitude of 7.3 struck the west of Iran, causing massive damage in SarpoleZahab and surrounding areas. In this study, the Horizontal to Vertical Spectral Ratio (HVSR or H/V technique) of strong ground motion recorded at 60 stations is used for identifying of site response and subsequent site classification. Very clear amplification is observed at stiff and soft soil sites. Empirical equations from the literature relating frequency of HVSR peak to average shear wave velocity in the upper 30m(V_S30), commonly used as a proxy for site classification, were found to be unreliable, making site classification difficult. To overcome this problem, a soft computing method based on the Generalized Regression Neural Networks (GRNN) algorithm is used. This algorithm relies on HVSR curves at the recording stations and uses pattern recognition against a set of pre-defined curves for different site types. In this approach, site characterization is based on the overall features of the HVSR curves rather than just the peak frequency. The stations are classified into four different site classes, and the results are consistent with local geotechnical information, where available. Rather than relying on V_S30, which has been found deficient in many studies, and is known to have difficulty in reliably estimating when geotechnical data is lacking, site classification proposed in this study uses the overall characteristics of HVSR curves of response spectra of recorded strong motion data. This method is proposed as a more reliable and direct method for site characterization in seismically hazardous regions.

Original language	English
Article number	105574
Journal	Engineering Geology
Volume	270
DOIs	https://doi.org/10.1016/j.enggeo.2020.105574
Publication status	Published - 5 Jun 2020

Bibliographical note

Funding Information:
The authors gratefully acknowledge the Building and Housing Research Center (BHRC) for providing the strong-motion records used in this study. The second author acknowledges support from the University of Iceland research fund. We are thankful to the two anonymous reviewers and the editor, whose comments and feedback were very useful in improving the manuscript.

Publisher Copyright:
© 2020 Elsevier B.V.

Other keywords

Generalized regression neural network
Horizontal-to-vertical (H/V) spectral ratio
Site amplification
Site effect
Site resonant frequency
The 2017 Ezgeleh earthquake

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10.1016/j.enggeo.2020.105574

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@article{3911514e19fd4147a3dd1cfa661ed6f0,

title = "A new method of seismic site classification using HVSR curves: A case study of the 12 November 2017 Mw 7.3 Ezgeleh earthquake in Iran",

abstract = "On the 12th of November 2017, a strong earthquake with a moment magnitude of 7.3 struck the west of Iran, causing massive damage in SarpoleZahab and surrounding areas. In this study, the Horizontal to Vertical Spectral Ratio (HVSR or H/V technique) of strong ground motion recorded at 60 stations is used for identifying of site response and subsequent site classification. Very clear amplification is observed at stiff and soft soil sites. Empirical equations from the literature relating frequency of HVSR peak to average shear wave velocity in the upper 30m(VS30), commonly used as a proxy for site classification, were found to be unreliable, making site classification difficult. To overcome this problem, a soft computing method based on the Generalized Regression Neural Networks (GRNN) algorithm is used. This algorithm relies on HVSR curves at the recording stations and uses pattern recognition against a set of pre-defined curves for different site types. In this approach, site characterization is based on the overall features of the HVSR curves rather than just the peak frequency. The stations are classified into four different site classes, and the results are consistent with local geotechnical information, where available. Rather than relying on VS30, which has been found deficient in many studies, and is known to have difficulty in reliably estimating when geotechnical data is lacking, site classification proposed in this study uses the overall characteristics of HVSR curves of response spectra of recorded strong motion data. This method is proposed as a more reliable and direct method for site characterization in seismically hazardous regions.",

keywords = "Generalized regression neural network, Horizontal-to-vertical (H/V) spectral ratio, Site amplification, Site effect, Site resonant frequency, The 2017 Ezgeleh earthquake",

author = "Saman Yaghmaei-Sabegh and Rajesh Rupakhety",

note = "Funding Information: The authors gratefully acknowledge the Building and Housing Research Center (BHRC) for providing the strong-motion records used in this study. The second author acknowledges support from the University of Iceland research fund. We are thankful to the two anonymous reviewers and the editor, whose comments and feedback were very useful in improving the manuscript. Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",

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AU - Rupakhety, Rajesh

N1 - Funding Information: The authors gratefully acknowledge the Building and Housing Research Center (BHRC) for providing the strong-motion records used in this study. The second author acknowledges support from the University of Iceland research fund. We are thankful to the two anonymous reviewers and the editor, whose comments and feedback were very useful in improving the manuscript. Publisher Copyright: © 2020 Elsevier B.V.

PY - 2020/6/5

Y1 - 2020/6/5

N2 - On the 12th of November 2017, a strong earthquake with a moment magnitude of 7.3 struck the west of Iran, causing massive damage in SarpoleZahab and surrounding areas. In this study, the Horizontal to Vertical Spectral Ratio (HVSR or H/V technique) of strong ground motion recorded at 60 stations is used for identifying of site response and subsequent site classification. Very clear amplification is observed at stiff and soft soil sites. Empirical equations from the literature relating frequency of HVSR peak to average shear wave velocity in the upper 30m(VS30), commonly used as a proxy for site classification, were found to be unreliable, making site classification difficult. To overcome this problem, a soft computing method based on the Generalized Regression Neural Networks (GRNN) algorithm is used. This algorithm relies on HVSR curves at the recording stations and uses pattern recognition against a set of pre-defined curves for different site types. In this approach, site characterization is based on the overall features of the HVSR curves rather than just the peak frequency. The stations are classified into four different site classes, and the results are consistent with local geotechnical information, where available. Rather than relying on VS30, which has been found deficient in many studies, and is known to have difficulty in reliably estimating when geotechnical data is lacking, site classification proposed in this study uses the overall characteristics of HVSR curves of response spectra of recorded strong motion data. This method is proposed as a more reliable and direct method for site characterization in seismically hazardous regions.

AB - On the 12th of November 2017, a strong earthquake with a moment magnitude of 7.3 struck the west of Iran, causing massive damage in SarpoleZahab and surrounding areas. In this study, the Horizontal to Vertical Spectral Ratio (HVSR or H/V technique) of strong ground motion recorded at 60 stations is used for identifying of site response and subsequent site classification. Very clear amplification is observed at stiff and soft soil sites. Empirical equations from the literature relating frequency of HVSR peak to average shear wave velocity in the upper 30m(VS30), commonly used as a proxy for site classification, were found to be unreliable, making site classification difficult. To overcome this problem, a soft computing method based on the Generalized Regression Neural Networks (GRNN) algorithm is used. This algorithm relies on HVSR curves at the recording stations and uses pattern recognition against a set of pre-defined curves for different site types. In this approach, site characterization is based on the overall features of the HVSR curves rather than just the peak frequency. The stations are classified into four different site classes, and the results are consistent with local geotechnical information, where available. Rather than relying on VS30, which has been found deficient in many studies, and is known to have difficulty in reliably estimating when geotechnical data is lacking, site classification proposed in this study uses the overall characteristics of HVSR curves of response spectra of recorded strong motion data. This method is proposed as a more reliable and direct method for site characterization in seismically hazardous regions.

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KW - Horizontal-to-vertical (H/V) spectral ratio

KW - Site amplification

KW - Site effect

KW - Site resonant frequency

KW - The 2017 Ezgeleh earthquake

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JO - Engineering Geology

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M1 - 105574

ER -

A new method of seismic site classification using HVSR curves: A case study of the 12 November 2017 Mw 7.3 Ezgeleh earthquake in Iran

Abstract

Bibliographical note

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