Chapter 4: Ground-Motion Response in Urban Los Angeles from the 2019 Ridgecrest Earthquake
4.3 Spectral Acceleration from Measured Data
4.3.3 Correlation with site parameters
Figure 4.14. Contoured SA amplification maps, for the M7.1 event. (A) T=1 s. (B) T=3 s. (C) T=6 s. (D) T=8 s. The station locations are marked with black dots.
We calculate the depth-to-basement and Vs30 for each site from grid values using a triangulation- based linear interpolation. The depth-to-basement grid values are from Shaw et al. (2015). A depth- to-basement value corresponds to the depth to crystalline rock / basement. Negative values correspond to surface outcrop (Figure 4.15A). The Vs30 grid values are from Thompson (2018) (Figure 4.15B) and account for constraints from multiple sources (site-specific measurements, surface geology, and topographic gradient). As discussed in Brown et al. (2002) there are a number of advantages to using slowness rather than velocity, hence in the following, we will be considering the shear-wave slowness in the top 30 meters, defined as Ss30 = 1/Vs30. This also allows us to better focus on the low Vs30 regions. We find the SA correlation with Ss30 to be stronger than the correlation with Vs30.
Figure 4.15C shows depth-to-basement plotted against Ss30 (and Vs30) at each station. A statistically significant correlation exists between the depth-to-basement and Ss30 values, with correlation coefficient π = 0.74 and the p-value π < 0.001 (between the depth-to-basement and Vs30 values these statistics are: π = -0.65 and π < 0.001). Sediments are highly correlated with basins and the Vs30 is affected by the existence of such sediments. The figure displays this built- in correlation between the Vs30 and the deeper structures. Another interesting feature of Figure 4.15C is the banded behavior of the Vs30 values. This is likely due to the fact that in the absence of site specific measurements, the Vs30 values account for surface geology and topographic gradient (Thompson, et al., 2014). The smoothness of the Vs30 dataset in combination with the high spatial density of the seismic networks, result in many stations having exactly the same Vs30 value even if their underlying soil conditions are not exactly the same.
Figure 4.15. Site parameter plots. (A) Sensor locations plotted on top of contoured depth-to- basement. (B) Sensor locations plotted on top of contoured Vs30. (C) Scatter plot of the depth-to-
basement plotted against Ss30 and Vs30 at the station locations. Circles: CSN stations, Diamonds: SCSN & CSMIP stations. Depth-to-Basement contours are marked at 1-km intervals.
Vs30 contours are marked at 100-m/s intervals.
Figure 4.16 shows the correlation between every stationβs observed SA and its corresponding depth-to-basement. As before, to quantitatively assess the correlations, we calculate the correlation coefficients (π) and the p-values (π). We find π = 0.079, 0.61, 0.75, and 0.74 for T=1, 3, 6, and 8 s respectively. The p-values are π = 6.45e-2 for T=1 s and π < 0.001 for T=3, 6, and 8 s. For T=1 s there is no correlation between SA and the depth-to-basement. However, for longer periods a correlation appears and sites with larger depth-to-basement have on average larger SAs. The p- values confirm that the correlation is statistically significant for the longer periods. The correlation is the strongest at 6 s. As one can see in Figure 4.16 it is not always the case that the largest SAs correspond to the areas of the deepest basement depth.
Figure 4.16. SA correlation with depth-to-basement, for the M7.1 event. (A) T=1 s. (B) T=3 s.
(C) T=6 s. (D) T=8 s. Damping ratio ΞΆ=5%. Circles: CSN stations, Diamonds: SCSN & CSMIP stations. The solid line shows the least-squares (LS) regression fit. The slope and intercept of the
LS regression line, and the standard deviation are provided in each subplot.
Figure 4.17 displays the correlation between every stationβs observed SA and its corresponding Ss30. We find π = 0.12, 0.43, 0.52, and 0.49 for T=1, 3, 6, and 8 s respectively. The p-values are π = 3.22e-3 for T=1 s and π < 0.001 for T=3, 6, and 8 s. A main feature of Figure 4.17 is the banded pattern of the Ss30, discussed previously, due to the smoothness of the Vs30 grid. This pattern makes it difficult to visually observe any correlation between SA and Ss30 but the statistical measures π and π suggest that for T=3, 6, and 8 s, there exists a weak but statistically significant correlation. Again, for T=1 s there is almost no correlation, while the correlation is the strongest for T=6 s.
The correlation of the observations with Vs30 was perhaps expected to be stronger for the shorter periods. Vs30 and SA have been found to have a strong correlation at T=1 s in empirical ground- motion data sets, such as the NGA-W2 dataset (Ancheta, et al., 2014). The repeated Vs30 values and banded Vs30 behavior for the spatially dense CSN stations, also discussed in Figure 4.15C, likely obscures the Vs30 scaling at the shorter length-scales (i.e., shorter periods). The observed stronger correlation between SA and Vs30 for the longer periods might also come as a surprise, due to the fact that from its definition the Vs30 measure only accounts for the local, near surface conditions. These correlation trends likely result from the inherent correlation of Vs30 with basement depth in the Los Angeles region, as demonstrated in Figure 4.15C. At the longer periods, the correlation between SA and depth-to-basement is found to be stronger than the correlation between SA and Ss30. Another possible reason, however, could be the hybrid approach used to derive the Vs30 values, where apart from site-specific Vs30 measurements, surface geological units and topographic gradient are also taken into account (Thompson, et al., 2014). It is possible that these larger-scale features result in the correlation observed for the longer periods.
Figure 4.17. SA correlation with Ss30, for the M7.1 event. (A) T=1 s. (B) T=3 s. (C) T=6 s. (D) T=8 s. Damping ratio ΞΆ=5%. Circles: CSN stations, Diamonds: SCSN & CSMIP stations. The
solid line shows the least-squares (LS) regression fit. The slope and intercept of the LS regression line, and the standard deviation are provided in each subplot.