M. Durmaz
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In geotechnical practice, the cone penetration test (CPT) is widely used for soil stratification; however, the large spacing between soundings makes it difficult to construct accurate two-dimensional (2D) subsurface cross-sections. Conventional Gaussian Process (GP) models are effective for spatial interpolation under stationary assumptions, but real CPT data often exhibit strong non-stationarity due to geological layering, limiting the applicability of GP-based models in practice. To address this limitation, this paper develops a Mixture of Gaussian Processes (MGP) framework for explicitly modeling the distinct spatial features of different soil layers. The MGP integrates layer-specific GPs with a probabilistic layer identification mechanism, enabling joint representation of stratigraphic boundaries and within-layer variability. The framework is validated using synthetic ground profiles of varying stratigraphic complexity and applied to real CPT data from the Groningen region of the Netherlands. Results show that the MGP generally improves prediction accuracy and uncertainty characterization relative to the GP, particularly in inclined or more complex stratigraphic settings where layer-dependent variability and boundary effects are important.
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In geotechnical practice, the cone penetration test (CPT) is widely used for soil stratification; however, the large spacing between soundings makes it difficult to construct accurate two-dimensional (2D) subsurface cross-sections. Conventional Gaussian Process (GP) models are effective for spatial interpolation under stationary assumptions, but real CPT data often exhibit strong non-stationarity due to geological layering, limiting the applicability of GP-based models in practice. To address this limitation, this paper develops a Mixture of Gaussian Processes (MGP) framework for explicitly modeling the distinct spatial features of different soil layers. The MGP integrates layer-specific GPs with a probabilistic layer identification mechanism, enabling joint representation of stratigraphic boundaries and within-layer variability. The framework is validated using synthetic ground profiles of varying stratigraphic complexity and applied to real CPT data from the Groningen region of the Netherlands. Results show that the MGP generally improves prediction accuracy and uncertainty characterization relative to the GP, particularly in inclined or more complex stratigraphic settings where layer-dependent variability and boundary effects are important.
Gaussian process regression is an effective method for the stochastic interpolation of geotechnical site data. However, a significant drawback of this method is its stationarity assumption, which is unrealistic given the existence of different soil layers. This assumption results in higher uncertainty in interpolation and poorer performance. To address this limitation, a mixture of Gaussian processes model is investigated for simultaneous layer identification and spatial interpolation in 2D. The model is based on the probabilistic assessment of layer boundaries and Gaussian processes for defining the statistical properties of the layers as well as spatial interpolation. The accuracy of the model is tested with real CPT profiles. The performance of the model is evaluated based on interpolation accuracy.
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Gaussian process regression is an effective method for the stochastic interpolation of geotechnical site data. However, a significant drawback of this method is its stationarity assumption, which is unrealistic given the existence of different soil layers. This assumption results in higher uncertainty in interpolation and poorer performance. To address this limitation, a mixture of Gaussian processes model is investigated for simultaneous layer identification and spatial interpolation in 2D. The model is based on the probabilistic assessment of layer boundaries and Gaussian processes for defining the statistical properties of the layers as well as spatial interpolation. The accuracy of the model is tested with real CPT profiles. The performance of the model is evaluated based on interpolation accuracy.
Stratification identification and spatial interpolation play a fundamental role in geotechnical site characterization. A unified approach is needed to perform these two tasks simultaneously to reduce overall uncertainty in site characterization. This paper explores the applicability of the Mixture of Gaussian Processes (MoGP) to address this gap, with a specific focus on characterizing and completing missing CPT data. The investigation encompasses both synthetic and real-world field CPT datasets and includes a comparison of the MoGP's interpolation accuracy with the use of a single GP for entire datasets. Additionally, the study examines the sensitivity of the model's performance with respect to the number of training data points. Although the interpolation performance of the MoGP model is promising with synthetic data, limitations appear in its application to real-site CPT data.
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Stratification identification and spatial interpolation play a fundamental role in geotechnical site characterization. A unified approach is needed to perform these two tasks simultaneously to reduce overall uncertainty in site characterization. This paper explores the applicability of the Mixture of Gaussian Processes (MoGP) to address this gap, with a specific focus on characterizing and completing missing CPT data. The investigation encompasses both synthetic and real-world field CPT datasets and includes a comparison of the MoGP's interpolation accuracy with the use of a single GP for entire datasets. Additionally, the study examines the sensitivity of the model's performance with respect to the number of training data points. Although the interpolation performance of the MoGP model is promising with synthetic data, limitations appear in its application to real-site CPT data.