The LiDAR-Based 3D Stratigraphic Model Calibrated with Limited Borehole Data

Chih Hsiang Yeh, Yu Chen Lu, C. Hsein Juang, Jia-Jyun Dong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A detailed 3D stratigraphic model is essential for designing underground structures such as tunnels, as it can reduce the risk of geotechnical failure. However, traditional methods of subsurface geological investigation, such as drilling and geophysical testing, may not yield sufficient stratigraphic data due to cost or testing limitations, resulting in a high degree of uncertainty in the developed 3D models. Recently, airborne LiDAR technology has provided high-resolution topographic data to help improve the accuracy of geological mapping. Still, reliable procedures to create 3D stratigraphic models have not yet been developed. This paper proposes a methodology for modeling stratigraphic bedding that combines LiDAR surface observation data, LiDAR-derived attitude data, and drilling (borehole) data to create engineering-scale, high-precision 3D stratigraphic models. First, regression analysis for stratigraphic beddings is performed using the LiDAR observation points exposed on the surface. Next, each stratigraphic bedding in 3D space is initially simulated with a multinomial mathematical model and calibrated with LiDAR-derived attitude data. Then, each stratigraphic bedding is further calibrated with the borehole-derived stratification. Finally, all the stratigraphic bedding models are integrated to yield a 3D stratigraphic model. In summary, this paper demonstrates the potential of LiDAR data in developing a reliable 3D stratigraphic model at the engineering application scale.

Original languageEnglish
Title of host publicationGeotechnical Special Publication
EditorsJianye Ching, Shadi Najjar, Lei Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Number of pages9
EditionGSP 347
ISBN (Electronic)9780784484968, 9780784484975, 9780784484982, 9780784484999
StatePublished - 2023
EventGeo-Risk Conference 2023: Advances in Modeling Uncertainty and Variability - Arlington, United States
Duration: 23 Jul 202326 Jul 2023

Publication series

NameGeotechnical Special Publication
NumberGSP 347
ISSN (Print)0895-0563


ConferenceGeo-Risk Conference 2023: Advances in Modeling Uncertainty and Variability
Country/TerritoryUnited States


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