Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression
Generally, soil properties are measured only at limited locations. To rationally estimate the spatial distribution of soil properties, it is preferable to effectively use all available measurement data, including indirect data. We propose a Gaussian process regression with multiple random fields tha...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Soils and Foundations |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0038080625000587 |
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| author | Yuto Tsuda Ikumasa Yoshida Shinichi Nishimura |
| author_facet | Yuto Tsuda Ikumasa Yoshida Shinichi Nishimura |
| author_sort | Yuto Tsuda |
| collection | DOAJ |
| description | Generally, soil properties are measured only at limited locations. To rationally estimate the spatial distribution of soil properties, it is preferable to effectively use all available measurement data, including indirect data. We propose a Gaussian process regression with multiple random fields that considers the cross-correlation between one of the random fields of direct data and indirect data, and show the application to simulated data and actual measured data. In the application, the direct data are of CPT tip resistance (qc), which was obtained within a narrow area, and the indirect data are of shear wave velocity (Vs) obtained by surface wave exploration, which were obtained over a wide area. We estimate the spatial distribution of qc from the limited qc and wide area Vs data. The estimation accuracy of the proposed method is evaluated by cross-validation, and its effectiveness is discussed. |
| format | Article |
| id | doaj-art-4d7f57ec82bc42e8ba5cfecab9999e4e |
| institution | DOAJ |
| issn | 2524-1788 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Soils and Foundations |
| spelling | doaj-art-4d7f57ec82bc42e8ba5cfecab9999e4e2025-08-20T03:17:52ZengElsevierSoils and Foundations2524-17882025-06-0165310162410.1016/j.sandf.2025.101624Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regressionYuto Tsuda0Ikumasa Yoshida1Shinichi Nishimura2Postdoctoral Researcher, School of Integrative Science and Engineering, Tokyo City University, Tokyo 158-8557, Japan; Corresponding author.Professor Emeritus, Department of Urban and Civil Engineering, Tokyo City University, Tokyo 158-8557, JapanDepartment of Civil Environmental Engineering, Okayama University, Okayama 700-8530, JapanGenerally, soil properties are measured only at limited locations. To rationally estimate the spatial distribution of soil properties, it is preferable to effectively use all available measurement data, including indirect data. We propose a Gaussian process regression with multiple random fields that considers the cross-correlation between one of the random fields of direct data and indirect data, and show the application to simulated data and actual measured data. In the application, the direct data are of CPT tip resistance (qc), which was obtained within a narrow area, and the indirect data are of shear wave velocity (Vs) obtained by surface wave exploration, which were obtained over a wide area. We estimate the spatial distribution of qc from the limited qc and wide area Vs data. The estimation accuracy of the proposed method is evaluated by cross-validation, and its effectiveness is discussed.http://www.sciencedirect.com/science/article/pii/S0038080625000587Shear wave velocityGaussian process regressionRandom fieldCPT tip resistanceIndirect data |
| spellingShingle | Yuto Tsuda Ikumasa Yoshida Shinichi Nishimura Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression Soils and Foundations Shear wave velocity Gaussian process regression Random field CPT tip resistance Indirect data |
| title | Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression |
| title_full | Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression |
| title_fullStr | Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression |
| title_full_unstemmed | Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression |
| title_short | Spatial distribution estimation by considering the cross-correlation between components with indirect data using Gaussian process regression |
| title_sort | spatial distribution estimation by considering the cross correlation between components with indirect data using gaussian process regression |
| topic | Shear wave velocity Gaussian process regression Random field CPT tip resistance Indirect data |
| url | http://www.sciencedirect.com/science/article/pii/S0038080625000587 |
| work_keys_str_mv | AT yutotsuda spatialdistributionestimationbyconsideringthecrosscorrelationbetweencomponentswithindirectdatausinggaussianprocessregression AT ikumasayoshida spatialdistributionestimationbyconsideringthecrosscorrelationbetweencomponentswithindirectdatausinggaussianprocessregression AT shinichinishimura spatialdistributionestimationbyconsideringthecrosscorrelationbetweencomponentswithindirectdatausinggaussianprocessregression |