JDR Vol.18 No.4 pp. 298-307
doi: 10.20965/jdr.2023.p0298


Assessment of Site Amplification Factors in Southern Lima, Peru Based on Microtremor H/V Spectral Ratios and Deep Neural Network

Hiroyuki Miura*,† ORCID Icon, Carlos Gonzales** ORCID Icon, Miguel Diaz** ORCID Icon, Miguel Estrada** ORCID Icon, Fernando Lazares** ORCID Icon, Zenon Aguilar** ORCID Icon, Da Pan* ORCID Icon, and Masashi Matsuoka*** ORCID Icon

*Graduate School of Advanced Science and Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan

Corresponding author

**Japan Peru Center for Earthquake Engineering Research and Disaster Mitigation (CISMID), National University of Engineering (UNI)
Lima, Peru

***School of Environment and Society, Tokyo Institute of Technology
Yokohama, Japan

November 30, 2022
March 16, 2023
June 1, 2023
site amplification factor, microtremor H/V spectral ratio, deep neural network, Lima

Evaluation of site amplification factors (SAFs) of seismic waves has been one of the important issues for evaluating seismic hazards. The authors have proposed a deep neural network (DNN) model in order to cost-effectively and accurately estimate SAF from microtremor horizontal-to-vertical spectral ratio (MHVR). In this study, we assessed the SAFs in southern Lima, Peru by estimating from MHVRs and DNN. First, we validated the applicability of the DNN model to Lima by estimating the SAFs from the MHVRs observed at seismic stations in Lima. From the comparison with the observed SAFs derived from spectral inversion technique, we confirmed that the SAFs in Lima were accurately estimated by the DNN model. The SAFs in the southern Lima including Chorrillos and Villa El Salvador districts were evaluated by applying the DNN model to the observed MHVRs at approximately 250 sites. We found that large amplifications at low frequency around 1 Hz were expected in the southeastern coastal areas formed by eolian sands whereas smaller amplification were estimated in the northwestern areas mainly located on alluvial deposits.

Cite this article as:
H. Miura, C. Gonzales, M. Diaz, M. Estrada, F. Lazares, Z. Aguilar, D. Pan, and M. Matsuoka, “Assessment of Site Amplification Factors in Southern Lima, Peru Based on Microtremor H/V Spectral Ratios and Deep Neural Network,” J. Disaster Res., Vol.18 No.4, pp. 298-307, 2023.
Data files:
  1. [1] D. Pan, H. Miura, T. Kanno, M. Shigefuji, and T. Abiru, “Deep-neural-network-based estimation of site amplification factor from microtremor H/V spectral ratio,” Bulletin of the Seismological Society of America, Vol.112, No.2, pp. 1630-1646, 2022.
  2. [2] D. Calderon, T. Sekiguchi, S. Nakai, Z. Aguilar, and F. Lazares, “Study of soil amplification based on microtremor and seismic records in Lima Peru,” J. of Japan Association for Earthquake Engineering, Vol.12, No.2, pp. 2_1-2_20, 2012.
  3. [3] D. Calderon, Z. Aguilar, F. Lazares, T. Sekiguchi, and S. Nakai, “Estimation of deep shear-wave velocity profiles in Lima, Peru, using seismometers arrays,” J. Disaster Res., Vol.8, No.1, pp. 252-258, 2013.
  4. [4] T. Sekiguchi, D. Calderon, S. Nakai, Z. Aguilar, and F. Lazares, “Evaluation of surface soil amplification for wider areas in Lima, Peru,” J. Disaster Res., Vol.8, No.1, pp. 259-265, 2013.
  5. [5] S. Quispe et al., “Estimation of S-wave velocity profiles at Lima City, Peru using microtremor arrays,” J. Disaster Res., Vol.9, No.5, pp. 931-938, 2014.
  6. [6] S. Quispe, H. Yamanaka, Z. Aguilar, F. Lazares, and H. Tavera, “Preliminary analysis for evaluation of local site effects in Lima City, Peru from ground motion data by using the spectral inversion method,” J. Disaster Res., Vol.8, No.1, pp. 243-251, 2013.
  7. [7] S. Quispe et al., “Evaluation of local site amplification in Lima, Peru from ground motion data,” Proc. of 16th World Conf. on Earthquake Engineering (16WCEE), 3567, 2017.
  8. [8] H. Kawase, F. Nagashima, K. Nakano, and Y. Mori, “Direct evaluation of S-wave amplification factors from microtremor H/V ratios: Double empirical corrections to ‘Nakamura’ method,” Soil Dynamics and Earthquake Engineering, Vol.126, 105067, 2019.
  9. [9] GitHub. [Accessed August 1, 2022]
  10. [10] D. J. Andrews, “Separation of source and propagation spectra of seven Mammoth Lakes aftershocks,” Proc. of Workshop XVI, The Dynamic characteristic of faulting, Inferred from Recording of Strong Ground Motion (Open-File Report 82-591), pp. 437-454, 1981.
  11. [11] T. Iwata and K. Irikura, “Source parameters of the 1983 Japan Sea earthquake sequence,” J. of Physics of the Earth, Vol.36, No.3, pp. 155-184, 1988.
  12. [12] D. Calderon, Z. Aguilar, F. Lazares, S. Alarcon, and S. Quispe, “Development of a seismic microzoning map for Lima City and Callao, Peru,” J. Disaster Res., Vol.9, No.5, pp. 939-945, 2014.
  13. [13] C. Gonzales, A. Sifuentes, F. Lazares, S. Quispe, and K. Huerta, “Vs profiles, H/V spectra and geotechnical classification as proxies of the soil dynamic behavior in Lima, Peru,” Proc. of 17th World Conf. on Earthquake Engineering (17WCEE), C003940, 2020.
  14. [14] G. Riveros, “Seismic Microzoning of Villa El Salvador District,” Undergraduate Thesis, National University of Engineering, 2022 (in Spanish).
  15. [15] M. Matsuoka, H. Miura, S. Midorikawa, and M. Estrada, “Extraction of urban information for seismic hazard and risk assessment in Lima, Peru using satellite imagery,” J. Disaster Res., Vol.8, No.1, pp. 328-345, 2012.

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Last updated on Sep. 29, 2023