International Journal of Geophysics
Volume 2016 (2016), Article ID 9305095, 11 pages
http://dx.doi.org/10.1155/2016/9305095

Research Article

Estimate for Southwest China

Yan Yu,¹ Walter J. Silva,² Bob Darragh,² and Xiaojun Li³

¹Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China
²Pacific Engineering and Analysis, 856 Sea View Drive, El Cerrito, CA 94530, USA
³Institute of Geophysics, China Earthquake Administration, Beijing 100081, China

Received 26 September 2015; Accepted 7 February 2016

Academic Editor: Marek Grad

Copyright © 2016 Yan Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Several methods were used to estimate from site profiles with borehole depths of about 20 m for the strong-motion stations located in Southwest China. The methods implemented include extrapolation (constant and gradient), Geomatrix Site Classification correlation with shear-wave velocity, and remote sensing (terrain and topography). The gradient extrapolation is the preferred choice of this study for sites with shear-wave velocity profile data. However, it is noted that the coefficients derived from the California data set are not applicable to sites in Southwest China. Due to the scarcity of borehole profiles data with depth of more than 30 m in Southwest China, 73 Kiknet profiles were used to generate new coefficients for gradient extrapolation. Fortunately, these coefficients provide a reasonable estimate of for sites in Southwest China. This study showed could be estimated by the time-average shear-wave velocity (average slowness) of only 10 meters of depth. Furthermore, a median estimate based upon Geomatrix Classification is derived from the results of the gradient extrapolation using a regional calibration of the Geomatrix Classification with . The results of this study can be applied to assign to the sites without borehole data in Southwest China.

1. Introduction

As ground motion records became more abundant, site amplification had been studied by many researchers. Hayashi et al. [1] proposed the average acceleration response spectra for various subsoil condition in Japan. Seed et al. [2] derived site-dependent spectra from 104 ground motion records obtained from 23 earthquakes, mostly in the western part of US. To meet the application of seismic engineering and measure the site amplification, (time-average shear-wave velocity with depth of 30 meters) was a principal parameter to represent site condition and widely used for site classification. Borcherdt [3] studied the relation between and for NEHRP recommended building code provisions. Hartzell et al. [4] derived a correlation between site amplification and from the aftershock records of the 1989 Loma Prieta earthquake. Five NGA ground motion prediction models [5] all used for site classification. Although cannot, of course, capture all of the physics controlling site amplification [6], is widely accepted by seismic engineers for its simplicity and low cost.

The most straightforward way to evaluate for a given site is to measure seismic velocities to a depth of at least 30 meters. For a number of reasons, engineering seismic exploration was always not available for target areas. Then, was estimated from proxies, which may be based on geomorphology [7, 8], geology [9], or geotechnical site categories [10]. Since many seismic explorations would not reach the depth of 30 meters, the empirical relationship between and at shallower depths was derived from borehole data at target areas [11–15]. All these proxies were approximations and had obvious regional limitations. Stewart et al. [16] found the empirical relationship always overestimate for Greece. Boore et al. [12] found that the difference of empirical relationship between Japan and the other places resulted from the difference of site classification of borehole data.

On May 12, 2008, an earthquake with 7.9 occurred in Wenchuan county, Sichuan province, China, which resulted in widespread damage and a great number of casualties. During the Wenchuan earthquake, the National Strong-Motion Observation Network System (NSMONS) of China obtained 1,350 components of strong-motion records from the main shock, including records from 437 free-field stations in 17 provinces, municipalities, and autonomous regions, 1 topographic array (8 stations) in Sichuan province, and 2 temporary arrays (10 monitoring sites) for structural response at the Kunming mobile observatory [17–19]. After the main shock, 59 mobile instruments were deployed to record ground motion and structural response from strong aftershocks [20]. 15,903 components of digital strong-motion records were obtained from 949 aftershocks, in which 9750 components were recorded by portable instruments [20–22].

In order to use these records from the Wenchuan main shock and aftershocks in the NGA (Next-Generation Attenuation) project of PEER (Pacific Earthquake Engineering Research Center), an estimate of of the recording sites is needed. is a widely used parameter for classifying site condition regarding its ability to amplify seismic shaking. However, in China, the site classification is based on , depth of 20 m, and the thickness of overlying soil over rock according to the Chinese site classification in the seismic design building code. During the construction of strong-motion stations in China, the investigation of the site condition only provides the information on the overlying soil layers of depths less than 20 m, including the thickness and shear-wave velocity of soil layers. In the site investigation, layers with shear-wave velocity greater than 500 m/s are considered bedrock. Therefore, most of the depths of drilling holes are less than 30 m at the strong-motion station sites. As a result, an important issue for the strong-motion stations in China is the use of shear-wave velocity profiles with borehole depth less than 30 m to assign a .

In this study, 147 shear-wave velocity profiles measured with borehole technique at the strong-motion station in Southwest China (Sichuan and Gansu provinces) were used. These strong-motion stations are located in the heavily damaged region of the Wenchuan earthquake. The locations of these stations are shown in Figure 1. The Appendix gives the borehole depth, shear-wave velocity at the bottom of borehole, and whether or not the drilling reached bedrock (defined as  m/s). There are 6 stations with borehole depth less than 10 m, 32 stations with borehole depth between 10 m and 20 m, and 109 stations with borehole depth larger than 20 m. This study estimates for these 147 stations based on the measured shear-wave velocity profiles to the depth available.

Figure 1: Station distribution around the main shock.

2. Methodology

2.1. Simple Extrapolation

In this method, we assume that the shear-wave velocity from the bottom of the borehole to 30 m is constant at measured at the borehole bottom. The time-average shear-wave velocity (average slowness, [23]), named , was computed from the equation

where the travel time was given by

is the shear-wave velocity at depth Z.

Since is based on the measured velocity profile data, it is taken as the reference value of for comparison with other empirical estimates.

is typically less than the actual in general as generally increase with increasing depth. In cases, where the borehole depth is greater than 20 meters, the difference is generally small.

2.2. Geomatrix Classification Assignment

The PEER NGA database [10] includes 561 sites with borehole data. All the sites were assigned with a Geomatrix Classification according to geological and geographic conditions. The NGA Geomatrix Site Classification criteria are given as follows:

 A, rock: instrument on rock ( > 600 m/s) or <5 m of soil over rock. B, shallow (stiff) soil: instrument on/in soil profile up to 20 m thick overlying rock. C, deep narrow soil: instrument on/in soil profile at least 20 m thick overlying rock, in a narrow canyon or valley not more than several km wide. D, deep broad soil: instrument on/in soil profile at least 20 m thick overlying rock, in a broad valley. E, soft deep soil: instrument on/in deep soil profile with average < 150 m/s.

A median and standard deviation have been obtained for each Geomatrix Classification based upon a global database of measured profiles. Table 1 gives the median and standard deviation.

Table 1: Median and standard deviation for NGA database [10] profiles based on Geomatrix Classification bins.

Geomatrix Classification
Median (m/s)
(m/s)

A
660
324
74

B
424
211
97

C
338
70
44

D
274
110
306

E
191
61
40

The Geomatrix Classification for the 147 Southwest China sites is 9 A sites, 52 B sites, 83 C sites, and 3 D sites.

Next, we compare the median shear-wave velocity profiles of Southwest China (SWC) with NGA profiles based on Geomatrix Classification. The result is shown in Figure 2. In the depth range of 0 to 5 m, the median of SWC is less than that of NGA profiles on average. The near surface soils in SWC are softer than those in NGA. However, at depths below 5 m, the median of SWC profiles is similar to that of NGA profiles, except for site class B. The site class B profile of SWC has a slightly larger median than NGA.

Figure 2: Comparison of median between Southwest China (SWC) profiles and NGA profiles.

Since the median shear-wave velocity profiles of SWC sites are similar to those of the NGA sites, we may assign the median to the sites with the same Geomatrix Classification, (), with a small bias on the high side. Also, we can assign to sites without profile but with a Geomatrix Classification, again slightly biased high.

2.3. Boore Extrapolation

Boore [11] used a set of 135 borehole profiles with depths greater than or equal to 30 m to estimate a correlation between and , which is time-average shear-wave velocity for a profile to depth . is given by

where is the borehole depth and is given by (2).

Boore [11] found that the logarithm of against logarithm of could be fit by a straight line, given by

Table 2 gives the regression coefficients and standard deviation for Boore’s correlation equation from 135 California profiles.

Table 2: Coefficients of (4) for 135 California profiles [11].

(m)

10
0.0421
1.0292
0.0713

11
0.0221
1.0341
0.0647

12
0.0126
1.0352
0.0594

13
0.0142
1.0318
0.0548

14
0.0123
1.0297
0.0501

15
0.0138
1.0263
0.0459

16
0.0139
1.0237
0.0422

17
0.0196
1.0190
0.0394

18
0.0249
1.0144
0.0364

19
0.0256
1.0117
0.0332

20
0.0254
1.0095
0.0302

21
0.0253
1.0072
0.0270

22
0.0269
1.0044
0.0241

23
0.0222
1.0042
0.0208

24
0.0169
1.0043
0.0177

25
0.0115
1.0045
0.0147

26
0.0066
1.0045
0.0115

27
0.0025
1.0043
0.0084

28
0.0008
1.0031
0.0055

29
0.0004
1.0015
0.0027

Since the standard deviation of Boore’s estimate is less than 8 percent for stations with borehole depth larger than 10 m, we apply Boore’s empirical model, which is generated from California data, to the SWC profiles to estimate (), given by

where the superscript Cal means that the model is based on California data.

A comparison of to in the Appendix shows that Boore’s estimate for profiles with borehole depth greater than 20 m is very close to , which has an average bias of 0.006. However, for the 32 profiles with borehole depth between 10 and 20 m, has an average bias of 0.139 (positive bias reflects an underprediction) relative to . The result suggests that the coefficients of Boore’s estimate derived from California profiles may not be applicable to SWC profiles.

Boore indicates that of SWC profiles is larger than that of California profiles in the same depth and the velocity gradients of these two regions are different, which results in the underestimate of . Similar to Boore, in order to eliminate the difference of velocity gradients between the regions, we want to derive the coefficients from similar profiles.

Linear and cubic fit to Kiknet profiles were performed to fit against . The cubic fit is given by the following equation:

According to (6), can be estimated by

Table 3 gives the coefficients and standard deviation of linear and cubic fit based on Boore’s correlation equation applied to 73 Kiknet measured profiles with Geomatrix Classification, and Figure 3 displays the fit.

Table 3: Coefficients and natural logarithm of the standard deviation for (4) and (6) from Kiknet profiles.

Depth (m)

5
1.3412
0.5626
1.0320
1.3924
−0.5117
0.0906
0.1174
0.1158

6
1.2498
0.5975
−1.2168
3.8378
−1.3922
0.1955
0.1120
0.1110

7
1.1650
0.6288
−1.1351
3.6074
−1.2640
0.1756
0.1062
0.1055

8
1.1071
0.6492
0.0978
2.1222
−0.6846
0.1022
0.1018
0.1012

9
1.0009
0.6878
−2.0070
4.3394
−1.4621
0.1928
0.0956
0.0952

10
0.9056
0.7223
−3.2943
5.6294
−1.8935
0.2410
0.0896
0.0894

11
0.8111
0.7559
−4.0334
6.2874
−2.0859
0.2595
0.0837
0.0835

12
0.7307
0.7840
−4.0876
6.2020
−2.0116
0.2464
0.0784
0.0782

13
0.6465
0.8132
−4.6981
6.7207
−2.1550
0.2592
0.0732
0.0729

14
0.5709
0.8389
−4.9266
6.8299
−2.1532
0.2550
0.0681
0.0677

15
0.5018
0.8617
−5.3861
7.2075
−2.2547
0.2638
0.0627
0.0622

16
0.4401
0.8816
−5.5568
7.2781
−2.2480
0.2600
0.0579
0.0573

17
0.3824
0.8999
−5.6979
7.3279
−2.2381
0.2563
0.0533
0.0525

18
0.3315
0.9156
−5.6043
7.1366
−2.1460
0.2433
0.0489
0.0480

19
0.2848
0.9296
−4.8807
6.2744
−1.8177
0.2028
0.0445
0.0436

20
0.2440
0.9415
−4.0266
5.2892
−1.4511
0.1583
0.0404
0.0395

21
0.2055
0.9525
−2.9989
4.1264
−1.0243
0.1071
0.0362
0.0353

22
0.1724
0.9616
−1.8671
2.8692
−0.5696
0.0532
0.0323
0.0315

23
0.1424
0.9695
−1.1240
2.0489
−0.2768
0.0191
0.0288
0.0280

24
0.1155
0.9763
−0.5169
1.3890
−0.0458
−0.0073
0.0255
0.0248

25
0.0883
0.9832
−0.1184
0.9585
0.1017
−0.0236
0.0223
0.0217

26
0.0622
0.9897
0.2604
0.5551
0.2377
−0.0383
0.0194
0.0189

27
0.0413
0.9943
0.2788
0.5517
0.2275
−0.0355
0.0163
0.0158

28
0.0216
0.9985
0.2809
0.5694
0.2087
−0.0315
0.0136
0.0133

29
0.0033
1.0023
0.4227
0.4434
0.2391
−0.0331
0.0117
0.0115

Figure 3: The linear fit and cubic fit to the Kiknet data in 4 depth bins, 5, 10, 20, and 28 m.

Figure 3 shows that the goodness of cubic fit is slightly better than linear fit for some stiff sites, especially for depth less than 5 m. When the borehole depth is greater than 10 meters, the difference between these two fits becomes negligible.

Next, we assess whether conditioning a Geomatrix category can improve the estimate. We classify 73 Kiknet sites as 60 rock sites with Geomatrix class A or class B and 13 soil sites with Geomatrix C or D. We use both linear and cubic fit to the rock sites and soil sites separately. Table 4 gives the comparison of the bias and standard deviation between the simple extrapolation, normal fit (all site pooled), and Geo fit (rock and soil bins).

Table 4: Comparison of bias and standard deviation between the normal fit and Geo fit to Kiknet profiles.

Depth bins
Bias of
Standard deviation
Bias of
Standard deviation
Bias of
Standard deviation
Bias of
Standard deviation
Bias of
Standard deviation

Simple extrapolate
Simple extrapolate
Linear fit
Linear fit
Geolinear fit
Geolinear fit
Cubic fit
Cubic fit
Geocubic fit
Geocubic fit

5
0.4945
0.3137
0.0074
0.2603
0.0049
0.2428
0.0068
0.2570
0.0028
0.2395

10
0.1670
0.1518
0.0051
0.1998
0.0036
0.1859
0.0068
0.1990
0.0045
0.1855

15
0.0664
0.0790
0.0026
0.1416
0.0014
0.1342
0.0028
0.1411
0.0014
0.1326

20
0.0240
0.0438
0.0015
0.0912
0.0007
0.0868
0.0026
0.0896
−0.0007
0.0827

28
0.0029
0.0249
0.0005
0.0310
0.0003
0.0301
0.0004
0.0303
0.0021
0.0291

Bias = .

Table 4 shows that the bias and standard deviation decrease with an increase of depth and that of Geo fit, conditioning on Geomatrix categories, provides slightly superior results to that of the normal fit.

We use the normal linear fit, which is defined as , to estimate for three reasons. First, it is simple. Second, the bias and standard deviation of the normal linear fit to the 20 m depth bin are only 0.0015 and 0.0912, respectively. The maximum error is just nine percent. At shallow depth, 10 m depth bin, the maximum error is only 20 percent, which is acceptable for many applications. Third, the improvement of Geo fit and cubic fit is not significant and the procedure is more complicated than normal fit due to the large number of coefficients.

2.4. Topographic Slop and Terrain-Based Estimate

The USGS earthquake hazards program developed an approach based on the similarity of geology and topography to provide a first-order assessment of [7]. It is assumed that the slope of topography, or gradient, could be diagnostic of , because more competent (high-velocity) materials are more likely to maintain a steep slope whereas deep basin sediments are deposited primarily in environments with low gradients. Correlation between topographic slop data and regional is built to estimate . We go to http://earthquake.usgs.gov/hazards/apps/vs30/custom.php and choose a location boundary including all the 147 Southwest China recording sites; estimate on each grid point is generated. at the closest grid point, whose distance to the target station is less than 0.6 km, is assumed as topographic estimate of , named .

Yong built a correlation between and California terrain-based units, which are derived from 1 km spatial resolution (SRTM30) digital elevation model. Based on California terrain-based units, Yong made an estimate of for Southwest China, named . Considering the difference of terrain between California and Southwest China, Yong makes a small change to to provide an alternative estimate of , named .

3. Comparisons and Conclusion

We have six types of estimates for Southwest China: , , , , , and . Since is obtained from the shear-wave velocity profile, we use as a reference to calculate the bias and standard deviation of the five other types of estimates. Table 5 summarizes the results.

Table 5: Bias and standard deviation of the five other types of estimates relative to .

Bias (ln)
−0.0704
−0.0656
−0.3189
−0.3451
−0.4434

0.0796
0.2003
0.2769
0.2444
0.4062

Table 5 indicates that two kinds of terrain-based estimates have large bias and standard deviation. We believe this is mainly due to the large difference of terrain slop between Southwest China and California. It is the same with topographic estimate. Because of the difference of geographic and topographic condition between these two regions, the topographic estimate shows more than 40 percent bias and huge standard deviation. The bias of is small whereas the standard deviation is about 30 percent. This is because there are only five categories in the Geomatrix Classification scheme and there is considerable within-category variability (Table 1). However, for the sites without profile data, provides a largely unbiased estimate of .

The bias and standard deviation of are both small, which verify that Boore’s model is a good method to estimate . However, the coefficients must be attained from profiles with similar geological conditions and similar velocity gradient, that is, local or regional calibration. Our results indicate that the geological condition of Sichuan province of China is similar to that of Japan. Hence, we use to estimate site effect for ground motion prediction equation.

In addition, Table 4 shows that can be estimated by average shear-wave velocity to 10 m depth with a bias of 0.0051 and σ of only 0.1998. Since the NGA models [5] use the natural logarithm of to estimate site effect, 20 percent difference of can make only five percent difference to the site effect, which is suitable for ground motion prediction and engineering applications.

Based on for 145 profiles, we get a median for the SWC profiles. Table 6 gives for Geomatrix Classification A, B, C, and D sites, which can be used to assign to SWC sites without profile data.

Table 6: Median for WCS sites.

Geomatrix Classification
Median (m/s)

A
553.40
79.23
9

B
412.29
69.23
52

C
353.73
63.72
83

D
296.83
72.54
3

Appendix

See Table 7.

Table 7

Station code
Depth of borehole (m)
at the bottom of borehole (m/s)
Bedrock at borehole bottom
Geomatrix Classification

51AXD
11.80
878
Yes
B
405
424
280
383
—
—
374

51AXT
12.80
510
Yes
B
331
424
281
376
388
547
362

51AXY
22.00
475
No
C
368
338
371
404
—
—
595

51BTD
22.20
436
No
C
334
338
337
368
547
547
540

51BTT
22.30
403
No
C
273
338
268
296
519
519
525

51BXY
19.80
618
Yes
B
318
424
288
332
519
519
760

51BXZ
18.20
658
Yes
B
379
424
341
394
519
519
760

51CXQ
4.90
321
Yes
A
321
660
—
564
402
402
360

51DCN
22.10
504
No
C
284
338
267
295
547
547
572

51DJA
22.00
453
No
C
287
338
276
304
—
—
332

51DXY
22.00
431
No
C
342
338
347
379
374
374
398

51DYB
15.20
539
Yes
B
374
424
345
418
388
388
355

51GYS
12.20
517
Yes
B
345
424
290
385
519
519
557

51GYZ
20.10
1207
Yes
B
388
424
326
366
519
519
465

51GZT
22.00
350
No
C
244
338
240
266
519
519
415

51GZX
22.00
325
No
C
337
338
373
406
402
519
431

51HDD
9.00
639
Yes
A
502
660
—
547
—
—
602

51HDQ
13.00
689
Yes
B
414
424
336
423
—
—
747

51HDX
14.50
651
Yes
B
362
424
297
376
—
—
719

51HLB
12.00
659
Yes
B
370
424
278
373
—
—
760

51HLD
9.00
579
Yes
B
431
424
—
471
—
—
760

51HLF
21.50
311
No
C
290
338
311
346
—
—
760

51HLW
12.00
564
Yes
B
403
424
355
449
—
—
760

51HLY
13.50
802
Yes
B
417
424
324
411
—
—
479

51HSD
21.00
496
No
C
340
338
331
367
519
519
760

51HSL
21.00
227
No
C
266
338
316
351
519
402
760

51HYJ
22.30
515
No
C
449
338
470
507
547
519
497

51HYQ
22.40
392
No
C
322
338
331
362
519
519
760

51HYU
20.30
677
Yes
A
441
660
424
469
—
—
271

51HYW
22.50
521
No
C
401
338
407
442
519
519
760

51HYY
19.40
515
Yes
B
414
424
426
475
519
519
760

51JLN
22.00
547
No
C
415
338
416
451
519
519
583

51JLT
15.30
520
Yes
B
311
424
267
337
547
547
760

51JYC
15.00
1070
Yes
B
466
424
357
430
497
388
311

51JYD
10.20
603
Yes
A
435
660
367
475
328
388
184

51JYH
22.00
633
Yes
B
320
424
296
326
547
547
760

51JYW
20.00
887
Yes
B
459
424
415
459
—
—
376

51JZB
22.00
465
No
C
328
338
323
354
519
519
760

51JZG
22.00
451
No
C
304
338
296
326
519
519
760

51JZW
22.00
607
No
C
428
338
422
457
519
519
760

51JZY
22.00
485
No
C
321
338
312
342
519
519
760

51KDG
22.00
330
No
C
269
338
275
303
519
519
536

51LBD
13.20
503
Yes
B
345
424
304
391
519
519
484

51LBH
13.20
503
Yes
B
345
424
304
391
519
519
559

51LDD
22.00
400
No
C
336
338
346
378
519
519
760

51LDJ
22.00
450
Yes
C
313
338
308
338
519
519
760

51LDL
22.00
365
No
C
308
338
318
349
519
519
760

51LDS
22.00
360
No
C
352
338
380
414
519
519
760

51LHT
20.50
375
No
C
327
338
346
387
519
519
732

51LSF
9.80
1130
Yes
B
605
424
—
517
547
388
760

51LSH
9.80
1660
Yes
B
858
424
—
649
328
547
682

51LSJ
10.70
876
Yes
A
598
660
498
587
374
393
363

51LXM
21.00
359
No
C
287
338
292
326
519
519
760

51LXS
20.20
452
No
C
311
338
302
341
519
519
760

51LXT
21.00
424
No
C
317
338
316
351
519
519
760

51LXY
20.20
257
No
C
269
338
308
347
—
—
760

51MCL
20.50
1050
Yes
B
460
424
409
453
519
519
568

51MED
16.00
941
Yes
B
489
424
408
475
519
519
760

51MES
12.00
482
Yes
B
389
424
380
473
—
—
760

51MEZ
20.10
474
Yes
B
363
424
364
406
519
519
760

51MNA
22.00
529
No
C
492
338
525
563
519
519
760

51MNC
22.00
516
No
C
438
338
453
490
519
547
760

51MNH
22.00
527
No
C
349
338
339
371
547
547
404

51MNJ
22.00
410
No
C
388
338
415
450
388
547
448

51MNL
22.00
500
No
C
323
338
312
343
519
388
419

51MNM
22.00
456
No
C
342
338
342
374
547
547
496

51MNZ
22.00
526
No
C
374
338
370
403
519
519
760

51MXD
21.00
358
No
C
268
338
267
299
519
519
760

51MXN
21.00
494
No
C
387
338
391
430
519
519
760

51MYL
22.00
375
No
C
310
338
319
349
—
—
324

51MYS
22.00
415
Yes
B
300
424
297
327
388
547
474

51NNH
22.00
432
No
C
267
338
256
283
—
—
760

51NNL
22.00
369
No
C
302
338
309
339
519
519
760

51NNS
22.00
280
No
C
267
338
287
316
519
519
760

51PGD
22.00
380
No
C
265
338
260
287
—
—
688

51PGL
22.00
413
No
C
331
338
337
368
519
519
760

51PGQ
22.00
557
No
C
284
338
263
290
519
519
760

51PJD
22.00
433
No
C
350
338
357
389
402
497
267

51PJW
22.00
440
No
C
312
338
308
338
363
374
216

51PWM
25.00
753
Yes
B
373
424
357
376
519
519
589

51PZF
22.00
700
Yes
A
573
660
588
628
—
—
760

51PZT
22.00
750
Yes
A
448
660
427
463
—
—
760

51PZW
22.00
800
Yes
B
411
424
381
415
—
—
453

51QCD
20.00
893
Yes
B
394
424
345
386
—
—
611

51QLY
22.00
747
Yes
B
486
424
472
508
547
519
527

51SFB
20.00
548
Yes
B
355
424
338
379
519
519
476

51SMC
22.00
340
No
C
309
338
326
357
519
519
760

51SMK
22.00
405
No
C
363
338
382
416
519
519
760

51SML
22.00
395
Yes
C
336
338
348
380
519
519
760

51SMM
22.00
320
No
C
279
338
291
320
519
519
760

51SMW
22.00
405
No
C
281
338
276
305
519
519
760

51SMX
22.00
400
No
C
310
338
312
343
519
519
760

51SPA
21.00
473
Yes
C
342
338
338
374
519
519
527

51TQL
22.00
1066
Yes
B
529
424
489
526
519
519
760

51WCW
20.10
485
Yes
C
357
338
353
395
—
—
760

51XCC
22.00
351
No
D
277
274
280
309
363
388
224

51XCH
22.00
522
No
C
386
338
385
418
547
547
518

51XCL
22.00
504
No
C
324
338
313
343
547
547
348

51XCT
22.00
510
Yes
B
389
424
390
424
—
—
449

51XCY
21.00
358
Yes
C
249
338
243
274
547
547
606

51XDG
22.00
507
No
C
353
338
347
379
519
519
573

51XDM
22.00
700
No
C
405
338
384
417
519
547
760

51XJB
21.00
430
No
C
327
338
328
364
—
—
760

51XJD
21.00
498
No
C
343
338
334
370
519
519
760

51XXC
22.00
431
No
D
336
274
340
372
459
459
726

51YAD
22.00
721
Yes
B
245
424
216
240
—
—
253

51YAL
22.00
1168
Yes
B
544
424
497
535
519
519
372

51YAM
22.00
877
Yes
A
577
660
561
600
547
388
760

51YAS
22.00
694
Yes
B
422
424
403
437
519
547
760

51YBA
22.00
760
Yes
B
475
424
456
493
—
—
589

51YBG
15.10
1302
Yes
A
739
660
631
693
—
—
516

51YBH
22.00
566
Yes
B
358
424
345
377
—
—
376

51YXX
22.00
364
No
C
304
338
313
343
547
547
630

51YXZ
22.00
444
No
C
348
338
352
385
547
547
757

51YYJ
22.00
555
Yes
B
384
424
376
410
—
—
760

51YYM
22.00
550
No
C
347
338
334
365
388
547
282

51YYW
22.50
420
No
C
248
338
238
264
388
459
430

51ZJJ
22.30
523
Yes
B
354
424
348
380
519
519
760

51ZJQ
20.80
520
Yes
B
346
424
338
379
519
547
524

62ANY
30.00
562
No
C
425
338
425
425
—
—
519

62BAS
23.00
530
Yes
C
307
338
293
318
519
519
687

62DAT
13.50
532
Yes
B
432
424
438
522
388
388
348

62ERT
13.00
528
Yes
B
371
424
330
417
246
519
333

62GLA
30.00
523
Yes
C
229
338
229
229
402
402
368

62GXT
24.00
510
Yes
C
256
338
243
262
402
519
403

62HEP
30.00
290
No
C
238
338
238
238
547
519
605

62HEZ
20.00
540
Yes
B
338
424
318
358
—
—
589

62HJI
28.00
506
Yes
C
372
338
373
381
—
—
563

62JAI
30.00
540
No
C
282
338
282
282
388
519
269

62JCH
22.00
550
No
C
477
338
498
535
—
—
306

62KLE
16.00
562
Yes
B
351
424
311
376
519
519
339

62LJB
25.00
540
No
C
358
338
354
373
328
547
304

62MXT
22.00
638
Yes
C
301
338
275
303
402
547
378

62PAN
30.00
530
Yes
C
343
338
343
343
519
388
548

62PJY
30.00
284
No
C
249
338
249
249
—
—
320

62QCH
28.00
510
No
C
362
338
362
370
374
345
322

62SHW
22.00
510
No
C
384
338
385
419
519
519
760

62TCH
16.00
790
Yes
B
402
424
332
397
519
519
698

62TSH
22.00
540
Yes
C
408
338
409
443
547
547
574

62WUD
28.00
262
No
D
221
274
223
228
519
519
522

62XGU
24.00
548
Yes
C
298
338
285
306
519
547
325

62XHS
26.00
530
Yes
B
341
424
337
351
—
—
642

62XIC
30.00
506
No
C
284
338
284
284
388
374
356

62YGX
20.00
525
Yes
B
311
424
288
327
—
—
544

62YLG
12.00
810
Yes
B
517
424
423
513
—
—
666

62ZNI
9.00
730
Yes
B
387
424
—
363
519
402
760

62ZPU
20.00
516
Yes
B
406
424
411
456
547
519
475

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The records used in this study are provided by the National Strong-Motion Networks Center of China. The authors give their thanks to the National Strong-Motion Networks Center of China and all of the relevant managers and experts. This work is financially supported by the National Natural Science Foundation of China (51278469, 51308509).

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