1 1 2 3 Journal of Geochemistry, Geophysics, Geosystems 4 Supporting Information for 5 6 Quantifying near-field and off-fault deformation patterns of the 1992 Mw 7.3 Landers earthquake 7 C. Milliner1, J. Dolan1, J. Hollingsworth2, S. Leprince3, A. Ayoub3, and C. Sammis1. 1 8 University of Southern California, Los Angeles, CA, United States, 2 9 3 10 ARUP consultants - London, United Kingdom, California Institute of Technology, Pasadena, CA, United States 11 12 Contents of this file 13 14 Text S1 15 Figures S1 to S17 16 Table S1 17 18 Introduction 19 The supplementary text includes detailed results of our synthetic tests, which are a similar but 20 more detailed analysis than the study of Michel and Avouac (2006). The supplementary figures 21 are split into two parts, the first are results relating to our synthetic tests (Figures S1-S4), the 1 2 22 second (Figures S5-S17) are results from our statistical analysis of the control of various 23 parameters on our COSI-Corr displacement, off-fault deformation (OFD) and fault-zone width 24 (FZW) data. For these synthetic tests we use two different images taken at different times in 25 order to incorporate the effect of different images textures and quality. 26 The COSI-Corr displacement (n = 1057), OFD (n = 280) and FZW (n = 1060) data are 27 acquired from stacked profiles from the correlation maps. The tested parameters we considered 28 include the inferred age of alluvial fans (proxy for degree of lithification), horizontal distance to 29 nearest bedrock exposure (a proxy for sediment thickness), fault strike relative to an optimal 30 orientation (a proxy for stress field), and fault length (a proxy for structural maturity). We infer 31 the ages of alluvial fans from the development of desert varnish, surface texture and relative 32 amount of incision between fan surfaces from false-color composite aerial photography and 33 Google Earth imagery. The horizontal distance to bedrock is determined from the same geologic 34 maps used in the main article. We also analyze how the COSI-Corr displacement, FZW and OFD 35 scale with each other. 36 The figures here present useful information on how the tested parameters show a control 37 (or lack of) with our data that serves as a supplement to the results in the main text. The majority 38 of the parameters presented here yield in most cases no discernable relation with our surface 39 deformation data. However, several of these comparisons do appear to exhibit a systematic 40 relationship. Specifically figure 10 shows a weak correlation between our data (COSI-Corr 41 displacement, OFD and FZW) with the fault length. As might be expected longer faults can host 42 larger displacements and wider fault-zones, while OFD should decrease as the fault matures and 43 lengthens. 2 3 44 We also provide a table (Table S1), which contains all the measurements made and used 45 in our statistical analysis that were extracted from our correlation maps. Note there are numerous 46 nan values in the OFD column, which represents no OFD was found, because we could only 47 measure OFD where a COSI-Corr measurement could be sufficiently matched with a nearby 48 field measurement at that location. 49 50 Text S1 51 Synthetic tests 52 During the correlation process, the correlation window analyses a subset of pixels within 53 the input aerial image and finds similar pixel pattern in the corresponding image that brackets the 54 event. Therefore the correlation window essentially averages the tectonic deformation over the 55 region defined by the window size. From this process of averaging, the sliding window is 56 thought to smooth the resulting correlation maps, thus adding an ‘artificial’ FZW to the real 57 width. Therefore, before we can measure the FZW reliably, we first need to understand the 58 correlation process. Specifically we need to constrain the magnitude of the artificial ‘smoothing’ 59 and understand whether this process is linear, predictable, and therefore correctable. We also 60 perform additional tests, the results of which constitute a powerful tool that uniquely constrains 61 the measurement bias and precision when subjectively estimating the FZW and displacement 62 from the stacked profiles. 63 Test A – Correlation window 3 4 64 The correlation process measures the average displacement over a given correlation 65 window [Leprince et al., 2007a; 2007b]. On a first approximation, the correlation process can 66 then be modeled as a simple convolution such as: 67 68 dobs dtr hcorr (S1) 69 70 Where hcorr is the convolution kernel, which is in our case a Hanning window, dtr is the 71 theoretical displacement to be measured, * represents the convolution operator, and dobs is the 72 displacement observed through the correlation process. By the properties of the convolution, the 73 support of dobs is therefore the sum of the supports of dtr and hcorr. In our case, the FZW can be 74 modeled as the support of the derivative of the offset across the fault displacement, such that: 75 76 d obs dtr hcorr x x (S2) 77 78 79 d FZWobs supp obs x dtr supp supp hcorr dx (S3) 80 81 82 FZWobs FZWth supp hcorr (S4) 83 4 5 84 If no OFD where to be present, the theoretical offset would be an ideal step function, and 85 its derivative an ideal Dirac delta function of null support [see equation (S2)]. A fault with no 86 OFD would be measured with a FZW equal to the support of the correlation window, and 87 generally, the observed FZW is biased by the support of the correlation window. Knowing the 88 correlation window, we can therefore correct the observed measurement of the FZW to recover 89 the true FZW. In practice, because the Hanning window quickly tapers down at the edges, its 90 effective support can be expected to be about half its exact support. We therefore determine this 91 correction term empirically. 92 To determine the artificial FZW (hcorr) on a fault of known displacement and width, we 93 imposed a synthetic earthquake in one of our post-event aerial image. In MATLAB we simply 94 shift one half of a post-event, aerial image by a known amount, thereby imitating a rupture on a 95 fault. We correlate the artificially dextrally sheared image with a different non-disturbed pre- 96 event image (Figure S1a) using the same processing procedure in COSI-Corr that we use to 97 produce our real dataset [Leprince et al., 2007a; 2007b]. We measured the FZW from the 98 correlation images using stacked profiles in the same manner as we implemented in our true 99 dataset. To shift image pixels by a non-integer amount, we applied a cubic interpolation to one 100 side of the deformed image. 101 We performed 39 synthetic tests with an array of faulting styles, widths and 102 displacements expected in a real environment. Figure S2 shows as expected; the observed 103 synthetic FZW (dobs) is systematically larger than the true synthetic FZW (dtr) by an amount 104 approximately half the window size. Figure S2 shows our empirically derived calibration 105 function with slope of 1.04, which unequivocally shows the linear and predictable nature of this 106 problem. We found for multiple tests that supp(dcorr) is consistently 18 pixels, which is illustrated 5 6 107 by the goodness-of-fit for our linear model. Therefore, to determine the true FZW (dtr), no matter 108 the true width of deformation, one can use the calibration function to correct for hcorr by simply 109 subtracting supp(dcorr) from the observed FZW (dh). Importantly, the synthetic tests reveal the 110 observed step signal seen in the stacked profiles cannot be produced by any other process except 111 by strain release occurring over a finite width. 112 Knowing the synthetic displacement and FZW value allows robust estimation of any 113 measurement bias, because we can directly compare the values to those subjectively estimated. 114 When shearing an image synthetically, we applied uniform displacement to the entire fault, 115 which also has a constant FZW along-strike. Repeatedly measuring the displacement and FZW 116 along-strike of our synthetic fault across different parts of the correlation image therefore gives 117 multiple independent measurements. This consequently yields an empirical distribution of the 118 FZW and displacement measurement. When measuring both the FZW and displacement, as 119 described below, the true values were not revealed to the user, in order to not influence the 120 measurements. 121 Test B - FZW measurement uncertainty 122 We performed 71 independent measurements of the FZW of the synthetic fault using 123 stacked profiles, shown in Figure S3. The synthetic fault has a known, pre-determined FZW of 124 60 m. Subjectively picking the FZW of a fault multiple times expectedly produces an empirical 125 distribution which is Gaussian, with a mode that exactly matches the true FZW. These tests 126 demonstrate that when subjectively estimating the FZW there is minor measurement bias 127 present, with ~2.00 m underestimation of the true FZW. From the Gaussian distribution seen in 128 Figure S3 we derive an empirical measurement uncertainty of + 12 m (2σ). This empirical 6 7 129 uncertainty value is consistent for any width of fault-zone because it is related to the correlator 130 window size, which is constant during the correlating process. 131 132 Test C – Displacement uncertainty 133 We also charaterize the measurement distribution for the displacement measurements 134 from the synthetic tests, the results of which are shown in Figure S4. We apply two tests, one for 135 a fault with supra-pixel movement of 2 m displacement and another with sub-pixel movement of 136 50 cm displacement. The tests indicate there is minor amount of measurement bias, with a small 137 overestimation of the true displacment by 0.01 m. Both the sub- and supra-pixel displacement 138 tests yield the same behavior with a 1 sigma measurement precision of + 0.07 m and + 0.05 m, 139 respectively, which is in agreement with the results of Michel and Avouac (2006). The very tight 140 precision and nearly zero measurement bias is reflective of the robustness of the correlation 141 procedure, the quality of the aerial images, and the use of profile stacking to suppress noise. 142 Furthermore, Figure S4a shows that subjectively estimating the displacement produces a small 143 amount of artificial, along-strike slip variability + 0.05 m at the 1 σ level. 144 145 146 147 148 7 8 149 150 Supplementary figures 151 152 Figure S1 (a) left: Synthetic correlation map of a single dextral fault striking north-south, with a 153 uniform, prescribed right-lateral displacement of 2m, with map co-ordinates in UTM. This 154 correlation is produced by using two-different images taken at different times (1989 and 1994) of 155 the same area, where the eastern half of the most recent image (1994) is displaced to the south. 156 The hashed rectangle shows the dimensions of the stacked profile orientated perpendicular to our 157 synthetic fault, with an along-strike width of 138 m and length of 1.75 km. (b) Right, shows the 158 synthetic deformation signal seen in the stacked profile. The x-axis is distance along profile 159 length. The y-axis is the pixel movement. The black line is the surface deformation from the 160 synthetic correlation map in a). Red sub-horizontal dashed lines are the linear regression fits to 161 the data. Displacement is defined as the difference between the 2 linear regressions where they 162 intercept the fault trace at x = 0, which in this profile is measured as 2.09 m. 8 9 163 164 165 166 167 168 169 Figure S2. Result from the synthetic tests measuring the FZW from the correlation maps in 170 comparison to the known, pre-determined true value. The linear regression is our calibration 171 function, which serves as an empirical tool to correct for the correlation window systematically 172 and artificially widening the true FZW. 9 10 173 174 175 176 177 Figure S3. Results from the synthetic tests repeatedly measuring the FZW along a fault of 178 constant known width of 60m. Blue histogram bars show the binned data measured from the 179 correlation maps. Red line shows our best model fit to the data with a Gaussian distribution. 180 Green vertical line shows the known, synthetic, pre-determined FZW value (60m). 181 10 11 182 183 184 Figure S4. Results from the synthetic tests repeatedly measuring the displacement along a fault 185 of constant, known displacement using stacked profiles. Two tests are performed. Test 1: is a 186 fault with uniform displacement of 0.5 m. Test 2: is a fault with uniform displacement of 2 m. (a) 187 Blue points are displacement measured plotted along-strike of our synthetic fault. Green 188 horizontal line shows the true, uniform displacement of the fault. The variability observed is a 189 reflection of subjectively estimating the displacement, noise in the data and geometric artifacts. 190 (b) Blue histogram bars show the binned displacement measured from the correlation maps. Red 191 line shows our best model fit to the data with a Gaussian distribution. Green vertical line shows 192 the known, synthetic, pre-determined displacement value. 193 194 11 12 195 196 197 198 199 200 Figure S5. Fault-zone width plotted against off-fault deformation. We find a from a Spearman’s rank a 201 value of 0.33, indicating that the FZW can only explain a small portion of the variabilty in the OFD data. 202 We do find however, a p-value < 0.01, indicating that there is a linear relation between the data and that 203 they are not independent. 204 12 13 205 206 Figure S6. COSI-Corr displacement (which represents the total displacement across the fault) versus 207 fault-zone width plotted. 208 13 14 209 210 Figure S7. COSI-Corr displacement (which represents the total displacement across the fault) versus off- 211 fault deformation. 212 213 14 15 214 215 Figure S8. a) Locations of fault-zone width measurement plotted with the geologic maps which illustrates 15 16 216 the range of near-surface materials used in the analysis [Dibblee, 1964a, 1964b; Dibblee, 1967a, 217 1967b, 1967c]. b) shows enlargement of southern termination of 218 16 17 219 Figure S9 a) Locations of off-fault deformation plotted with the geologic maps which illustrates the 220 range of near-surface materials used in the analysis [Dibblee, 1964a, 1964b; Dibblee, 1967a, 1967b, 221 1967c]. b) shows an enlargement of the Kickapoo stepover illustrating variation of OFD. 222 223 224 225 Figure S10. a) Off-fault deformation plotted as a function of horizontal distance to the nearest outcrop of 226 exposed bedrock which serves as a proxy for thickness of sediment. Larger distances from the nearest 227 range front would expectedly have thicker amounts of sediment and therefore larger OFD and FZW. b) 228 Shows fault-zone width plotted as a function of distance to bedrock, where location of bedrock exposures 229 are determinded from the geologic map (Figure S3). 230 231 232 233 17 18 234 235 236 Figure S11.a) Off-fault deformation plotted against the deviation of fault strike from a regional strike. 237 The regional strike represents the regional stress field, and thus the deviation a proxy of the faults optimal 238 orientation to the regional stress field. Faults with large deviations from zero (the presumed optimal 239 faulting strike) will likely be misaligned with the regional stress field and likely yield larger OFD and 240 wider FZWs. 18 19 241 242 Figure S12.a)Vertical offset plotted against off-fault deformation (%). Vertical deformation along a sub- 243 vertical fault-plane can in places producing ponding of sediment against the fault scarp and therefore 244 produce local areas of thick sediment. Thicker sediment would expectedly produce larger amounts of 245 OFD. 19 20 246 247 248 Figure S13. Fault-zone widths measured in alluvial fans of different inferred ages. Alluvial fan ages are 249 determinded relatively from multi-spectral aerial images and Google Earth and by analyzing the degree of 250 desert varnish development, surface texture and relative amount of incision of each fan. We designate 251 these with informal, local indexes as Q1 through Q4 from oldest to youngest, where degree of 252 consolidation is expected to increase with age. a) Shows FZW for Q1, b) FZW for fans measured in Q2, 253 c) FZWs measured in Q3 alluvial fans and d) FZWs measured in Q4 material. 254 20 21 255 256 257 Figure S14. Off-fault deformation (%) measured in alluvial fans of different inferred ages. Q4 is the 258 youngest fan, assumed to be the least consolidated and most likely to produce the largest OFD. Q1 is the 259 oldest observable fan along the surface rupture and assumed to be the most consolidated and likely to 260 produce least strain accommodated off the primary fault strand. Alluvial fan ages are determinded 261 relatively from multi-spectral aerial images and Google Earth and analyzing the degree of desert varnish 262 development, surface texture and relative amount of incision of each fan. a) Shows OFD for Q1, b) OFD 263 for fans measured in Q2, c) OFD measured in Q3 alluvial fans and d) OFD measured in Q4 material. 264 21 22 265 266 Figure S15 a) Mean OFD with 1 sigma error plotted for different zones of structural complexity with 267 colored dots denoting different lithologies found within each bin of structural complexity. b) Mean FZW 268 plotted with 1 sigma error for different zones of structural complexity with colored dots denoting different 269 lithologies found within each bin of structural complexity. We use three zones of structural complexity 270 instead of five, as used in the main text because this avoids parsing the data too finley and loosing sample 271 sizes. 22 23 272 273 Figure S16. a) The Fault length of 69 individual faults plotted against mean displacement found along the 274 given fault. Blue line shows best fit linear regression with R2 = 0.58 b) Fault length plotted against 275 maximum displacement measured along the individual fault. Blue line shows best fitting linear model 276 with R2 = 0.52. c) Mean OFD plotted as a function of length of fault. Blue line shows best fitting linear 277 model with R2 = 0.10. d) Shows fault length plotted against the mean fault-zone width of each fault, red 278 line shows best fitting linear model with R2 = 0.22. 279 23 24 280 281 282 Figure S17 (a) Subset of the north-south correlation result along the southern section of the 283 Johnson Valley fault, with location of profiles labelled. Color bar shows magnitude of pixel 284 movement, blue is movement to the north and yellow towards the south. (b) Google earth image 285 acquired of the same aerial extent as (a), illustrating the types of near-surface material and extent 286 of the alluvial channel and location of profile transects.(c) Profile drawn across the Holocene 287 alluvial channel showing the surface deformation signal taken from the correlation result in a). 288 The signal exhibits a wide fault-zone of 68 m, as delineated by the red, vertical dashed lines and 289 horizontal arrow. (d) Surface displacement signal from a profile drawn across the Landers 24 25 290 surface rupture from a), within a Pleistocene, more consolidated alluvial fan, which shows a 291 narrower 18 m fault-zone as delineated by the red, vertical dashed lines. 292 293 294 Table S1 295 Below is all 1060 measurements we made from our COSI-Corr correlation map. These 296 contain all the measurements made and used in our statistical analysis that were extracted from 297 our correlation maps. Note there are numerous nan values in the OFD column, which represents 298 no OFD was found, because we could only measure OFD where a COSI-Corr measurement 299 could be sufficiently matched with a nearby field measurement at that location. 300 UTM Easting UTM Northing Displacement (m) Displacemen t error (1σ) 550338 550428 550524 550590 550650 550704 550758 550812 550866 550926 550998 551058 551094 551142 551166 551190 3795930 3795798 3795660 3795528 3795390 3795258 3795120 3794988 3794856 3794718 3794586 3794448 3794316 3794184 3794046 3793914 3.06 3.29 3.04 3.07 2.95 2.77 2.79 2.90 2.85 2.62 2.75 2.88 2.87 2.23 1.55 1.73 0.07 0.07 0.06 0.06 0.07 0.06 0.06 0.06 0.07 0.07 0.07 0.06 0.07 0.07 0.09 0.07 Fault zone width (m) 174.00 234.00 132.00 48.00 66.00 42.00 24.00 222.00 198.00 30.00 66.00 156.00 90.00 93.50 30.00 30.00 Stack Azimut h OFD (m) OFD error (1 σ) 56.26 55.41 56.26 67.36 67.56 67.56 67.80 67.80 67.80 63.43 61.83 75.54 67.95 77.36 80.91 80.24 1.96 1.25 NaN NaN NaN NaN 0.43 NaN 0.05 NaN NaN NaN NaN NaN NaN NaN 0.12 0.16 NaN NaN NaN NaN 0.06 NaN 0.24 NaN NaN NaN NaN NaN NaN NaN 25 26 551220 551274 551316 551340 551358 551382 551406 551424 551448 551466 551490 551508 551532 551550 551556 551550 551568 551580 551592 551598 551586 551562 547848 547938 548034 548130 548226 548316 548412 548508 548598 548682 548772 548862 548946 549036 549126 549210 549294 549378 549468 3793776 3793644 3793506 3793374 3793242 3793104 3792972 3792834 3792702 3792564 3792432 3792294 3792156 3792018 3791880 3791736 3791586 3791442 3791310 3791178 3791040 3790902 3799998 3799866 3799734 3799596 3799464 3799332 3799200 3799062 3798930 3798798 3798660 3798528 3798396 3798258 3798126 3797994 3797862 3797724 3797592 1.36 1.85 3.35 3.34 3.58 3.27 3.26 3.31 2.97 3.12 3.19 3.39 3.08 2.96 2.89 2.87 3.04 3.07 3.10 2.79 2.75 2.54 0.13 0.14 0.17 0.21 0.50 0.67 0.84 0.85 0.76 0.65 0.69 0.87 0.96 1.00 1.13 1.24 1.37 1.25 1.31 0.09 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.06 0.06 0.07 0.06 0.06 0.07 0.07 0.07 0.06 0.07 0.06 0.06 0.07 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.07 0.06 0.06 0.06 0.06 0.06 0.06 42.00 48.00 408.00 474.00 468.00 516.00 654.00 678.00 342.00 372.00 552.00 372.00 102.00 72.00 66.00 120.00 126.00 144.00 66.00 138.00 102.00 60.00 78.00 102.00 186.00 138.00 102.00 102.00 168.00 126.00 78.00 30.00 48.00 48.00 48.00 30.00 36.00 48.00 48.00 48.00 42.00 69.52 68.19 80.24 80.91 80.46 80.24 81.29 80.91 81.29 81.02 81.29 81.02 81.07 81.35 97.54 84.30 84.30 84.08 84.64 93.73 94.35 100.69 55.02 54.64 54.68 54.76 55.02 54.64 54.68 54.76 57.29 57.01 56.69 56.26 57.09 57.29 56.85 56.41 59.46 55.41 56.41 NaN NaN NaN 0.42 0.71 NaN 0.76 NaN NaN NaN 0.69 NaN NaN NaN 0.01 NaN NaN NaN 0.30 NaN NaN NaN NaN NaN NaN NaN 0.26 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.88 NaN NaN NaN NaN NaN NaN NaN 0.12 0.12 NaN 0.53 NaN NaN NaN 0.31 NaN NaN NaN 0.61 NaN NaN NaN 0.59 NaN NaN NaN NaN NaN NaN NaN 0.06 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.08 NaN NaN NaN NaN 26 27 549558 549648 549738 549822 549900 550008 550062 550134 550206 550278 552462 552456 552444 552450 552456 552456 552420 552426 552438 552438 552438 552438 552432 552420 552414 552408 552390 552360 552366 552354 552342 552336 552336 552348 551556 551538 551538 551682 551676 551712 551700 3797460 3797322 3797190 3797058 3796920 3796656 3796524 3796386 3796254 3796122 3782508 3782382 3782250 3782124 3781992 3781866 3781746 3781614 3781488 3781308 3781152 3781020 3780882 3780750 3780606 3780480 3780348 3782754 3782628 3782490 3782358 3782232 3782100 3781986 3787134 3787020 3786900 3788178 3788046 3789468 3789348 1.28 1.29 1.42 1.37 1.05 2.67 2.78 2.51 2.63 3.24 0.38 0.39 0.54 0.86 1.26 0.99 1.01 0.98 0.97 1.01 0.60 0.48 0.70 0.67 0.33 0.49 0.50 1.03 1.26 0.43 0.71 0.51 0.39 0.20 0.88 1.28 1.10 0.52 0.71 0.32 0.32 0.06 0.06 0.07 0.07 0.10 0.06 0.06 0.08 0.07 0.07 0.08 0.09 0.10 0.10 0.12 0.06 0.07 0.09 0.08 0.08 0.07 0.07 0.07 0.07 0.08 0.07 0.07 0.07 0.14 0.08 0.10 0.09 0.07 0.08 0.10 0.19 0.30 0.07 0.15 0.07 0.07 48.00 66.00 102.00 84.00 30.00 222.00 84.00 24.00 54.00 150.00 54.00 108.00 90.00 54.00 60.00 150.00 312.00 372.00 420.00 546.00 600.00 636.00 768.00 582.00 672.00 810.00 864.00 102.00 198.00 108.00 72.00 48.00 36.00 60.00 102.00 174.00 198.00 54.00 42.00 54.00 36.00 55.98 55.56 57.21 59.34 60.40 67.56 64.53 61.78 62.00 56.49 83.07 98.71 86.61 90.00 86.32 98.76 98.43 83.18 89.38 90.00 90.00 93.56 93.56 93.56 93.34 94.35 95.36 79.31 95.08 94.74 93.68 93.73 86.32 86.32 99.09 100.20 77.52 96.43 88.30 107.13 93.39 0.54 0.69 NaN NaN 0.08 NaN NaN NaN NaN 1.14 NaN NaN 0.34 NaN NaN NaN NaN NaN 0.59 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.07 NaN NaN NaN 0.88 NaN 0.02 NaN NaN NaN 0.08 0.08 NaN NaN 0.15 NaN NaN NaN NaN 0.12 NaN NaN 0.11 NaN NaN NaN NaN NaN 0.11 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.13 NaN NaN NaN 0.21 NaN 0.13 NaN NaN NaN 27 28 551202 551196 551196 551196 551202 551202 551196 551190 551178 551166 551556 551502 551460 551424 551370 551298 551208 551094 550986 551520 551454 551688 551706 551598 551610 551616 551670 551688 551712 551748 551784 551820 551826 551790 551760 551742 551748 551748 551754 551772 551760 3789030 3788898 3788778 3788652 3788532 3788406 3788280 3788160 3788028 3787890 3790278 3790152 3790026 3789906 3789774 3789660 3789552 3789468 3789378 3790650 3790524 3785802 3785670 3790680 3790542 3790404 3790260 3790122 3789984 3789846 3789708 3789570 3789426 3789288 3789150 3789012 3788874 3788736 3788592 3788454 3788316 0.84 0.74 0.89 0.77 0.77 0.60 0.62 0.40 0.30 0.37 0.63 0.78 0.71 0.90 1.02 0.85 0.40 0.37 0.26 0.33 0.30 0.30 0.41 2.21 2.19 2.56 1.97 1.61 1.97 1.87 1.87 1.44 1.36 1.34 1.36 1.86 1.91 1.94 2.00 2.11 1.84 0.07 0.06 0.07 0.07 0.07 0.07 0.06 0.06 0.09 0.07 0.10 0.09 0.07 0.07 0.08 0.07 0.07 0.06 0.07 0.08 0.06 0.07 0.07 0.08 0.07 0.07 0.10 0.11 0.07 0.07 0.07 0.06 0.06 0.07 0.07 0.07 0.06 0.07 0.07 0.08 0.06 840.00 804.00 552.00 732.00 576.00 720.00 588.00 516.00 636.00 552.00 198.00 294.00 270.00 288.00 306.00 342.00 258.00 174.00 150.00 48.00 24.00 54.00 90.00 42.00 27.50 234.00 114.00 120.00 36.00 30.00 108.00 30.00 66.00 30.00 96.00 42.00 30.00 24.00 48.00 87.50 84.00 93.51 90.00 90.00 86.83 90.00 90.00 93.73 95.08 95.42 95.42 116.57 108.16 107.44 106.72 120.24 123.98 139.31 142.39 141.05 124.75 114.11 81.51 86.27 85.65 86.27 85.65 72.71 84.30 75.80 75.06 75.17 76.12 103.23 102.70 102.70 89.38 90.00 86.83 86.83 88.36 95.92 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.25 NaN 0.48 0.11 NaN NaN NaN 0.25 0.33 0.16 0.30 NaN NaN NaN 0.05 NaN NaN 1.07 0.51 NaN NaN NaN 0.84 NaN 0.24 NaN 0.67 NaN 0.24 0.64 1.11 0.84 NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.07 NaN 0.14 0.14 NaN NaN NaN 0.08 0.07 0.07 0.08 NaN NaN NaN 0.09 NaN NaN 0.14 0.25 NaN NaN NaN 0.14 NaN 0.21 NaN 0.16 NaN 0.36 0.12 0.22 0.22 28 29 551748 551748 551742 551724 551706 551688 551694 551688 551670 551676 551646 551628 551610 551604 551622 551640 551616 551598 551580 551592 551634 551670 551688 551712 551736 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