forked from bakerjw/NGAW2_correlations
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcorrPredictions.m
113 lines (89 loc) · 3.5 KB
/
corrPredictions.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
% compute reference correlation predictions for the considered IMs
% Jack Baker
% last modified 2 June 2016
clear; close all; clc;
load allIMsResids Periods SaIDX nonSaIDX
rhoPredAll = zeros(109,109);
%% Sa correlations
% from Baker, J. W., and Jayaram, N. (2008). ?Correlation of spectral acceleration values from NGA ground motion models.? Earthquake Spectra, 24(1), 299?317.
rhoPredAll(1:105,1:105) = BJ08_corrNew(Periods, Periods);
%% d5-75 correlations
imIDX = 106;
% Bradley, B. A. (2011a). ?Correlation of Significant Duration with Amplitude and Cumulative Intensity Measures and Its Use in Ground Motion Selection.? Journal of Earthquake Engineering, 15(6), 809?832.
%D5-75 coefficients
a=[0 -0.45 -0.39 -0.39 -0.06 0.16];
b=[0 -0.39 -0.39 -0.06 0.16 0.0];
e=[0.01 0.09 0.30 1.40 6.5 10.0];
for i=1:length(Periods)
for j=2:length(a)
if Periods(i)<=e(j)
rhoPredAll(i,imIDX)=a(j) + (b(j) -a(j)) /log(e(j) /e(j-1)) *log(Periods(i)/e(j-1));
break
end
end
end
%% d5-95 correlations
imIDX = 107;
% D5-95 coefficients
a95=[0 -0.41 -0.41 -0.38 -0.35 -0.02 0.23];
b95=[0 -0.41 -0.38 -0.35 -0.02 0.23 0.02];
e95=[0.01 0.04 0.08 0.26 1.40 6.0 10.0];
for i=1:length(Periods)
for j=2:length(a95)
if Periods(i)<=e95(j)
%rho_Ds595_SA_fit(i,1)=a95(j) + (b95(j)-a95(j))/log(e95(j)/e95(j-1))*log(Periods(i)/e95(j-1));
rhoPredAll(i,imIDX)=a95(j) + (b95(j)-a95(j))/log(e95(j)/e95(j-1))*log(Periods(i)/e95(j-1));
break
end
end
end
%% PGA correlations
imIDX = 108;
a_3=[1 0.97];
b_3=[0.895 0.25];
c_3=[0.06 0.8];
d_3=[1.6 0.8];
e_3=[0.2 10];
for i=1:length(Periods)
for j=1:2
if Periods(i)<=e_3(j)
%rho_PGA_SA_fit(i,1)=(a_3(j)+b_3(j))/2-(a_3(j)-b_3(j))/2*tanh(d_3(j)*log((Periods(i)/c_3(j))));
rhoPredAll(i,imIDX)=(a_3(j)+b_3(j))/2-(a_3(j)-b_3(j))/2*tanh(d_3(j)*log((Periods(i)/c_3(j))));
break
end
end
end
%% PGV correlations
imIDX = 109;
a=[0.73 0.54 0.80 0.76];
b=[0.54 0.81 0.76 0.7];
c=[0.045 0.28 1.1 5];
d=[1.8 1.5 3.0 3.2];
e=[0.1 0.75 2.5 10];
for i=1:length(Periods)
for j=1:4
if Periods(i)<=e(j)
%rho_PGV_SA_fit(i,1)=(a(j)+b(j))/2-(a(j)-b(j))/2*tanh(d(j)*log((Periods(i)/c(j))));
rhoPredAll(i,imIDX)=(a(j)+b(j))/2-(a(j)-b(j))/2*tanh(d(j)*log((Periods(i)/c(j))));
break
end
end
end
%% add lower-left terms to matrix
rhoPredAll(nonSaIDX,SaIDX) = rhoPredAll(SaIDX,nonSaIDX)';
%% lower right terms
% correlations compiled by JWB from Bradley papers for a number of IMs
% 1 2 3 4 5 6 7 8 9
% PGA AI PGV ASI SI D575 D595 CAV DSI
rho22 = [1 0.83 0.733 0.928 0.599 -0.442 -0.405 0.7 0.395; ...
0.83 1 0.73 0.81 0.68 -0.19 -0.2 0.89 0.51; ...
0.733 0.73 1 0.729 0.89 -0.259 -0.211 0.691 0.8; ...
0.928 0.81 0.729 1 0.641 -0.411 -0.37 0.703 0.376; ...
0.599 0.68 0.89 0.641 1 -0.131 -0.079 0.681 0.782; ...
-0.442 -0.19 -0.259 -0.411 -0.131 1 0.843 0.077 0.074; ...
-0.405 -0.2 -0.211 -0.37 -0.079 0.843 1 0.122 0.163; ...
0.7 0.89 0.691 0.703 0.681 0.077 0.122 1 0.565; ...
0.395 0.51 0.8 0.376 0.782 0.074 0.163 0.565 1];
consideredIDX = [6 7 1 3]; % these are the only IMs used in this work
rhoPredAll(nonSaIDX,nonSaIDX) = rho22(consideredIDX,consideredIDX);
save rhoPredAll rhoPredAll