Application of Geostatistical Sequential Simulation Methods for Probabilistic 3D Subsoil Modeling and Uncertainty Quantification Concept and Examples

被引:0
|
作者
Witty, Andreas [1 ]
Pena-Olarte, Andres A. [1 ]
Cudmani, Roberto [1 ]
机构
[1] Tech Univ Munich, Chair Soil Mech & Fdn Engn Rock Mech & Tunneling, Munich, Germany
关键词
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The application of geostatistical sequential simulation methods to build geomechanical models and quantify the corresponding uncertainty is well established in the oil and gas industry, whereas the majority of subsoil models created for civil engineering projects utilize deterministic interpolation methods where uncertainty cannot be evaluated. In this paper, the root causes of these uncertainties for subsoil model generation are briefly explained, and a concept for the consideration of these uncertainties by means of probabilistic subsoil modeling is presented. This makes it possible to statistically evaluate geotechnical risks in both the design and construction. Using two case studies, the use of the geostatistical method is demonstrated on the basis of site investigations consisting of (1) CPTs and (2) borehole logs. The case studies illustrate the great potential and advantages of this methodology of subsoil modeling compared to the conventional deterministic approach. Further developments are required for its implementation in practice, which are outlined in a brief outlook.
引用
收藏
页码:125 / 132
页数:8
相关论文
共 49 条
  • [1] Comparison of Gaussian and Indicator Based Sequential Simulation Methods for 3D Spatial Uncertainty Quantification in Subsoil Modeling Using Cone Penetration Tests
    Witty, Andreas
    Pena-Olarte, Andres A.
    Cudmani, Roberto
    GEO-RISK 2023: INNOVATION IN DATA AND ANALYSIS METHODS, 2023, 345 : 414 - 422
  • [2] Perspectives of 3D Probabilistic Subsoil Modeling for BIM
    Wiegel, Andreas
    Pena-Olarte, Andres A.
    Cudmani, Roberto
    GEOTECHNICS, 2023, 3 (04): : 1069 - 1084
  • [3] A geostatistical implicit modeling framework for uncertainty quantification of 3D geo-domain boundaries: Application to lithological domains from a porphyry copper deposit
    Fouedjio, Francky
    Scheidt, Celine
    Yang, Liang
    Achtziger-Zupancic, Peter
    Caers, Jef
    COMPUTERS & GEOSCIENCES, 2021, 157
  • [4] Application of uncertainty modeling in 2D and 3D object detection
    Wang M.
    Zhu B.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2023, 45 (08): : 2370 - 2376
  • [5] Robot Arm Simulation Using 3D Software Application with 3D Modeling, Programming and Simulation Support
    Abdullah, Sukarnur Che
    Jusoh, M. Azzeim M.
    Nawi, Nazri M.
    Amari, M. Dzulhelmy
    2016 INTERNATIONAL SYMPOSIUM ON MICRO-NANOMECHATRONICS AND HUMAN SCIENCE (MHS), 2016,
  • [6] Application of learning machine methods to 3D object modeling
    García, C
    Moreno, JA
    ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2002, PROCEEDINGS, 2002, 2527 : 536 - 545
  • [7] 3D modeling of geomechanical elastic properties in a carbonate-sandstone reservoir: a comparative study of geostatistical co-simulation methods
    Ostad, Mohsen Nazari
    Niri, Mohammad Emami
    Darjani, Mohsen
    JOURNAL OF GEOPHYSICS AND ENGINEERING, 2018, 15 (04) : 1419 - 1431
  • [8] Methods for capturing 3D shape and advantages in the application of 3D human modeling in ergonomics studies
    Batista, Denise
    Pereira, Fernando
    OCCUPATIONAL SAFETY AND HYGIENE - SHO2013, 2013, : 67 - 69
  • [9] Simulation of a Gaussian random field over a 3D surface for the uncertainty quantification in the composite structures
    S. Zein
    A. Laurent
    D. Dumas
    Computational Mechanics, 2019, 63 : 1083 - 1090
  • [10] Simulation of a Gaussian random field over a 3D surface for the uncertainty quantification in the composite structures
    Zein, S.
    Laurent, A.
    Dumas, D.
    COMPUTATIONAL MECHANICS, 2019, 63 (06) : 1083 - 1090