3D Geologic Modeling with Borehole Data by General Regression Neural Network

UM > Faculty of Science and Technology > DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING

Status	已發表Published
	3D Geologic Modeling with Borehole Data by General Regression Neural Network
	Zhou, W. H.; Zhao, L. S.; Chen, G. M.; Yuen, K. V.
	2018-05-01
Source Publication	Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management (6ISRERM)
Pages	279-284
Publication Place	Singapore
Publisher	NUSS kent ridge guild house
Abstract	It is a challenged task to examine the spatial variation of soil with sparse measured data. In this study, the general regression neural network (GRNN) method was presented to predict the 3D geologic soil profile of a region based on the data of seven boreholes. A probability vector was introduced to represent the soil type at the associated location. By ensuring the difference between the predicted soil type and the measured one less than 1% at these measured boreholes, the smoothing parameter in the GRNN method was determined. With the selected smoothing parameter, the regression model was developed using the borehole data and a 3D geologic model was established to present the spatial distribution of soil type. It indicates that the GRNN method can realize a simple and intuitive geologic modeling using only the spatial coordinates.
Keyword	general regression neural network vector variable of probability parameter optimization 3D geologic modeling
Language	英語English
The Source to Article	PB_Publication
PUB ID	36919
Document Type	Conference paper
Collection	DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding Author	Zhou, W. H.
Recommended Citation GB/T 7714	Zhou, W. H.,Zhao, L. S.,Chen, G. M.,et al. 3D Geologic Modeling with Borehole Data by General Regression Neural Network[C], Singapore:NUSS kent ridge guild house, 2018, 279-284.
APA	Zhou, W. H.., Zhao, L. S.., Chen, G. M.., & Yuen, K. V. (2018). 3D Geologic Modeling with Borehole Data by General Regression Neural Network. Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management (6ISRERM), 279-284.

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