Uncertainty assessment of a 3D geological model by integrating data errors, spatial variations and cognition bias

A 3D geological structural model is an approximation of an actual geological phenomenon. Various uncertainty factors in modeling reduce the accuracy of the model; hence, it is necessary to assess the uncertainty of the model. To ensure the credibility of an uncertainty assessment, the comprehensive impacts of multi-source uncertainties should be considered. We propose a method to assess the comprehensive uncertainty of a 3D geological model affected by data errors, spatial variations and cognition bias. Based on Bayesian inference, the proposed method utilizes the established model and geostatistics algorithm to construct a likelihood function of modeler's empirical knowledge. The uncertainties of data error and spatial variation are integrated into the probability distribution of geological interface with Bayesian Maximum Entropy (BME) method and updated with the likelihood function. According to the contact relationships of the strata, the comprehensive uncertainty of the geological structural model is calculated using the probability distribution of each geological interface. Using this approach, we analyze the comprehensive uncertainty of a 3D geological model of the Huangtupo slope in Badong, Hubei, China. The change in the uncertainty of the model during the integration process and the structure of the spatial distribution of the uncertainty in the geological model are visualized. The application shows the ability of this approach to assess the comprehensive uncertainty of 3D geological models.

Automated summary

The article proposes a method to assess the comprehensive uncertainty of a 3D geological model by incorporating factors such as data errors, spatial variations, and cognition bias, utilizing Bayesian inference and geostatistics algorithm. By considering multiple sources of uncertainties, a probability distribution of comprehensive uncertainty is constructed for the model, showing the ability to visualize changes in uncertainty during integration and spatial distribution of uncertainty in the geological model.