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Knowledge-based multiple point statistics for soil stratigraphy simulation
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DOI：10.1016/j.tust.2023.105475.png)
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Abstract
En 中文
An accurate representation of subsurface stratigraphy in the form of geotechnical profiles is a prerequisite for optimal underground design and safe construction. This paper proposes a novel knowledge-based multiple point statistics (KMPS) method to simulate the heterogeneities and spatial trends of subsurface strata and to generate geotechnical profiles. This approach incorporates domain knowledge in the form of cone penetration test and standard penetration test data as well as results from laboratory testing to increase simulation accuracy. A novel varying search template size and similarity threshold is adopted to alleviate the computational cost of the simulations. Ablation analyses are used to quantify the improvement of the proposed KMPS algorithm over existing baseline multiple point statistics algorithms. By way of example, the proposed approach is applied to real-world exploration data from the Suzhou No.6 metroline project. The results show that the computational cost of KMPS is effectively minimized whilst the simulation accuracy can be improved by up to 13.3%. The algorithm is further validated by extending the simulation to a test image. With sparse exploration data, the KMPS method is shown to effectively and efficiently predict soil profiles based on prior experience embodied by a training image, thus enabling a reduction in the number of field exploration boreholes.
Keywords:
Knowledge-based
MPS
Soil stratigraphy
Multiple source data
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