TY - JOUR
T1 - A two-dimensional approach to quantify stratigraphic uncertainty from borehole data using non-homogeneous random fields
AU - Cardenas, Ibsen Chivata
N1 - Publisher Copyright:
© 2023 The Author
PY - 2023/3/5
Y1 - 2023/3/5
N2 - In many instances, conditions in the subsurface are highly variable, while site investigations only provide sparse measurements. Consequently, subsurface models are usually inaccurate. These characteristics reflect uncertainty and mean significant engineering and environmental hazards. Such uncertainty should be quantified, in order, ultimately, to be reduced. To this end, in this paper, a new two-dimensional approach to quantify stratigraphic uncertainty is proposed and described. The approach is based on non-homogeneous random fields and considers categorical quantities to represent geological structures. Unlike other reported approaches in related literature, we provide evidence on the usefulness of the approach to: (i) comprehensively explore the many diverse and potential geological structures' configurations to exhaust uncertainty quantification; (ii) facilitate the representation of non-homogeneous fields in a computational inexpensive fashion; and (iii) ameliorate the complication of producing spatial Markov chains or Bayesian models to quantify uncertainty using finite difference equations. The proposed approach is demonstrated using a case analysis.
AB - In many instances, conditions in the subsurface are highly variable, while site investigations only provide sparse measurements. Consequently, subsurface models are usually inaccurate. These characteristics reflect uncertainty and mean significant engineering and environmental hazards. Such uncertainty should be quantified, in order, ultimately, to be reduced. To this end, in this paper, a new two-dimensional approach to quantify stratigraphic uncertainty is proposed and described. The approach is based on non-homogeneous random fields and considers categorical quantities to represent geological structures. Unlike other reported approaches in related literature, we provide evidence on the usefulness of the approach to: (i) comprehensively explore the many diverse and potential geological structures' configurations to exhaust uncertainty quantification; (ii) facilitate the representation of non-homogeneous fields in a computational inexpensive fashion; and (iii) ameliorate the complication of producing spatial Markov chains or Bayesian models to quantify uncertainty using finite difference equations. The proposed approach is demonstrated using a case analysis.
KW - Non-homogeneous random field
KW - Stratigraphic uncertainty
KW - Uncertainty quantification
U2 - 10.1016/j.enggeo.2023.107001
DO - 10.1016/j.enggeo.2023.107001
M3 - Journal article
SN - 0013-7952
VL - 314
JO - Engineering Geology
JF - Engineering Geology
M1 - 107001
ER -