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Integrated framework for characterization of spatial variability of geological profiles
Despite recent efforts to characterize the uncertainties involved with geological profiles and soil and rock properties, there has been limited study on their spatial correlations and how such features may be included in the engineering decision-making process. This paper presents an integrated framework for geostatisical analyses, which incorporates the restricted maximum likelihood (REML) method with the Matérn autocovariance model. Statistical tests are conducted including those for data normality, constant variance, and outliers, which ensure the fundamental assumptions of REML are not violated in the residual analyses of site data, meanwhile offering simple checks for potential errors in the dataset. The proposed approach also allows quantification of uncertainties in the subsurface profiles at the unsampled locations. The approach is illustrated through investigations on spatial correlation features of geological profiles at two project sites in Hong Kong. The number of irregularly spaced boreholes varies from 150 to 350 in the two cases, and the large volume of data enables the variations in rockhead levels to be studied through the proposed framework. In addition, the existence of geological faults in one of the sites is found to significantly affect the spatial variability of the rockhead level, as indicated by the reduced scales of fluctuation and spatial dependence, which corresponds to increased uncertainty in areas intersected by faults.
Integrated framework for characterization of spatial variability of geological profiles
Despite recent efforts to characterize the uncertainties involved with geological profiles and soil and rock properties, there has been limited study on their spatial correlations and how such features may be included in the engineering decision-making process. This paper presents an integrated framework for geostatisical analyses, which incorporates the restricted maximum likelihood (REML) method with the Matérn autocovariance model. Statistical tests are conducted including those for data normality, constant variance, and outliers, which ensure the fundamental assumptions of REML are not violated in the residual analyses of site data, meanwhile offering simple checks for potential errors in the dataset. The proposed approach also allows quantification of uncertainties in the subsurface profiles at the unsampled locations. The approach is illustrated through investigations on spatial correlation features of geological profiles at two project sites in Hong Kong. The number of irregularly spaced boreholes varies from 150 to 350 in the two cases, and the large volume of data enables the variations in rockhead levels to be studied through the proposed framework. In addition, the existence of geological faults in one of the sites is found to significantly affect the spatial variability of the rockhead level, as indicated by the reduced scales of fluctuation and spatial dependence, which corresponds to increased uncertainty in areas intersected by faults.
Integrated framework for characterization of spatial variability of geological profiles
Lo, M.K (author) / Leung, Y.F / Liu, W.F
2017
Article (Journal)
English
variation de la tête de roche , Borings , Geotechnology , Correlation analysis , rockhead variation , Geospatial data , Maximum likelihood method , Matérn covariance structure , méthode vraisemblance maximale restreinte , enquête du site , site investigation , Uncertainty , analyse résiduelle , Residual stresses , restricted maximum likelihood method , residual analysis , structure Matérn covariance , Case studies
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