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OpenAmp: An Open-Source Site Amplification Database to Unlock the Potential of Machine Learning

Chuanbin Zhu*, Dong-youp Kwak, Marco Pilz, Annabel Haendel, Junju Xie, Hiroshi Kawase, Fabrice Cotton

*Corresponding author for this work

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

1 Citation (Scopus)
35 Downloads (Pure)

Abstract

OpenAmp is an open-source, consistently curated, high-quality database of earthquake site amplification functions derived from instrumental recordings, accompanied by a comprehensive suite of site metadata, for 1,742 K-NET and KiK-net observation sites in Japan. The site metadata introduces several new parameters, including the site-specific spectral decay parameter (κ0), the standard deviation of the horizontal-to-vertical spectral ratio of earthquake recordings (σHV), and site dimensionality index (I1D3D). A distinctive feature of OpenAmp is the inclusion, for the first time, of a diverse set of vector-based parameters and proxies, including quarter-wavelength velocity (Vs,qwl), and frequency- and scale-dependent topographic metrics, which enable physics-consistent site parameterization. The (linear) site amplification functions are derived from a large dataset of weak-motion recordings via a nonparametric generalized inversion technique in the Fourier domain, and their correlations with diverse site parameters are analyzed. OpenAmp is unparalleled in its quality and completeness by any existing site database in the world. It enables a wide range of applications, including optimization of site parameterization, development and benchmarking of site-specific or geospatial amplification models, and testing of novel ideas. Ultimately, OpenAmp can advance data-driven approaches for modeling near-surface effects in seismic hazard assessments. The database and associated resources can be freely accessed via: https://doi.org/10.25398/rd.northumbria.29649008.
Original languageEnglish
Article numbere70054
Number of pages29
JournalEarthquake Spectra
Volume42
Issue number2
Early online date25 Mar 2026
DOIs
Publication statusPublished - 1 May 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • database
  • earthquake
  • hazard
  • machine learning
  • site amplification

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