- Ground-motion model is (form based on non-parametric analyses and previous studies): Equation for lnh from Yenier and Atkinson (2015a).
- Response parameter is pseudo-acceleration for 5% damping.
- V s,30 between 106 and 2100m∕s.
- Only include data from earthquakes with most reliable locations and magnitudes from a master database of
about 157 000 records from the KiK-net network from October 1997 to December 2011. Exclude subduction
events and those with focal depth > 35km. Only include surface records with measured V s,30. Only consider
records within their individual conservative passband and those records with signal-to-noise ratios ≥ 3
within this passband. Exclude data from earthquakes with < 3 usable records.
- Derive model to propose via a data-driven approach a better site classification than one based on V s,30.
- Data from 644 different sites.
- Data distribution dominated by distant > 50km records and events with Mw < 5. Hence site terms capture
linear site response.
- Use multi-step mixed-effects regression technique to estimate τ (inter-event), ϕS2S (inter-site) and ϕ0
(residual) variabilities. Firstly calibrate fR then use distance-corrected observations to find fM. This is
done to ensure coefficients unbiased by a few well-observed earthquakes or sites. Do not include site term
in the original function.
- Do not include a term related to faulting mechanism because did not find significant dependency within
non-parametric analyses on mechanism.
- Examine the intermediate residuals w.r.t. Mw, V s,30 and rjb. Compute mean, 15th and 85th percentiles
of residuals within 10 magnitude bins and 10 distance bins. Find no significant trends.
- Plot predicted and observed (from within small magnitude bins) ground motions for 0.02, 0.2 and 2s w.r.t.
distance. Plot observations colour-coded by distance and predictions w.r.t. magnitude. Find good match.
- Classify the 588 stations with δS2S available at all periods into 8 site clusters (number specified a priori)
with distinct mean site amplification functions and within-cluster site-to-site variability about 50% smaller
than overall ϕS2S using a spectral (k-means) clustering analysis (a type of unsupervised machine learning).
Choose 8 as number of clusters based on consideration of total within sum of squares (WSS) and the gap
statistic comparing the WSS change with that expected under an appropriate null reference distribution
of the data. Examine average site amplifications within each cluster and find clear separation. Compare
classification with previous classifications. Examine distribution of TG (predominant period) V s,10, V s,30
and H800 (depth to horizon with V s = 800m∕s) within each class. Find that some combinations of these
parameters can be used to classify stations into the 8 classes.