A geostatistical implicit modeling framework for uncertainty quantification of 3D geo-domain boundaries: Application to lithological domains from a porphyry copper deposit

• Published in: Computers & geosciences Vol. 157; p. 104931
• Main Authors: Fouedjio, Francky, Scheidt, Celine, Yang, Liang, Achtziger-Zupančič, Peter, Caers, Jef
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2021
• Subjects: Categorical spatial variable; Conditional simulation; covariance; data collection; decision making; Geostatistics; Implicit modeling; principal component analysis; uncertainty; Uncertainty modeling; Conditional simulation; Categorical spatial variable; Implicit modeling; Uncertainty modeling; Geostatistics
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2021.104931

The spatial modeling of geo-domains has become ubiquitous in many geoscientific fields. However, geo-domains’ spatial modeling poses real challenges, including the uncertainty assessment of geo-domain boundaries. Geo-domain models are subject to uncertainties due mainly to the inherent lack of knowledge in areas with little or no data. Because they are often used for impactful decision-making, they must accurately estimate the geo-domain boundaries’ uncertainty. This paper presents a geostatistical implicit modeling method to assess the uncertainty of 3D geo-domain boundaries. The basic concept of the method is to represent the underlying implicit function associated with each geo-domain as a sum of a random implicit trend function and a residual random function. The conditional simulation of geo-domains is performed through a step-by-step approach. First, implicit trend function realizations and optimal covariance parameters associated with the residual random function are generated through the probability perturbation method. Then, residual function realizations are generated through classical geostatistical unconditional simulation methods and added to implicit trend function realizations to obtain unconditional implicit function realizations. Next, the conditioning of unconditional implicit function realizations to hard data is performed via principal component analysis and randomized quadratic programming. Finally, conditional implicit function simulations are transformed to conditional geo-domain simulations by applying a truncation rule. The proposed method is constructed to honor hard data and stated rules of how geo-domains interact spatially. It is applied to a lithological dataset from a porphyry copper deposit. A comparison with the classical sequential indicator simulation (SIS) method is carried out. The results indicate that the proposed approach can provide a more reliable and realistic uncertainty assessment of 3D geo-domain boundaries than the traditional sequential indicator simulation (SIS) approach. •A new geostatistical implicit modeling framework.•Honor hard data and stated rules of how geo-domains interact spatially.•Integrate volume proportion constraints on the geo-domains.•Easily handle a large number of geo-domains.•Provide reliable uncertainty assessment of geo-domain boundaries.