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Figure/Table detail
A
K
-Means-Based Least Squares Collocation Method and Its Application in High-Precision Fitting and Estimation of Large-Scale Crustal Velocity Fields
Cheng WANG, Wei QU, Jiuyuan LI, Xingyou TANG, Guangyao WANG, Kai YANG
Journal of Geodesy and Geodynamics
, 2025, 45(
12
): 1294-1303. DOI:
10.14075/j.jgg.2024.11.525
经度/(°)
纬度/(°)
速度东向分量/mm·a
-1
速度北向分量/mm·a
-1
特征范围
31.905
13.953
33.160
60.820
特征标准差
10.290
4.274
6.157
17.644
Tab. 1
Characteristics of the original data
Other figure/table from this article
Fig. 1
Flow chart of KLSC algorithm
Fig. 2
GNSS crustal movement observation velocity field in the Qinghai-Xizang plateau region
Fig. 3
Relationship between the number of clusters and each clustering index before and after normalization of original data
Fig. 4
Velocity field clustering results based on KLSC algorithm
Fig. 5
Indexes distribution of 100 times clustering method
Fig. 6
Observed, simulated and residual velocityfields based on TLSC and KLSC algorithms
Tab. 2
The RMSE and correlation coefficient of fitted points and check points based onTLSC and KLSC algorithms
Fig. 7
Comparison of RMSE of fitted points andcheck points of TLSC and KLSC algorithms
Fig. 8
Residuals distribution and frequency distribution histograms of TLSC and KLSC algorithms
Tab. 3
The
t
-test results of the residuals of fitting points and check points based on TLSC and KLSC algorithms
Fig. 9
Variations of total residuals of fitting points and check points of 100 times clustering methodafter repeating the experiment 50 times
Fig. 10
The crustal continuous velocity field interpolated at intervals of 1° based on the TLSC algorithm
Fig. 11
The crustal continuous velocity field interpolated at intervals of 1° based on the KLSC algorithm
Tab. 4
Indices of each filtering results
Fig. 12
The crustal continuous velocity field components interpolated at intervals of 1° based on KLSC algorithm