How well can we predict earthquake site response so far? Machine learning vs physics-based modeling

Chuanbin Zhu*, Fabrice Cotton, Hiroshi Kawase, Kenichi Nakano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
1 Downloads (Pure)

Abstract

In site-specific site-response assessments, observation-based site-specific approaches requiring a target–reference recording pair or a regional recording network cannot be implemented at many sites of interest. Thus, various estimation techniques have to be used. How effective are these techniques in predicting site-specific site responses (average over many earthquakes)? To address this question, we conduct a systematic comparison using a large data set which consists of detailed site metadata and Fourier outcrop linear site responses based on observations at 1725 K-NET and KiK-net sites. We first develop machine learning (i.e. random forest (RF)) amplification models on a training data set (1580 sites). Then we test and compare their predictive powers at 145 independent testing sites with that of the one-dimensional (1D) ground response analysis (GRA). The standard deviation of residuals between observations and predictions, that is, between-site (site-to-site or inter-site) variability, is used as the benchmark. Results show that the machine learning model using a few predictor variables, surface roughness, peak frequency fP, HV, VS30, and depth Z2.5 achieves better performance than the physics-based modeling (GRA) using detailed 1D velocity profiles. This implies that machine learning can be more effective in using existing site information than 1D GRA which is inflicted by a high level of parametric and modeling uncertainties. This finding warrants the further exploration of machine learning in site effect characterization, especially on model transferability across different regions.
Original languageEnglish
Pages (from-to)478-504
Number of pages27
JournalEarthquake Spectra
Volume39
Issue number1
Early online date17 Aug 2022
DOIs
Publication statusPublished - 1 Feb 2023
Externally publishedYes

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