loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification

Abstract

Abstract. To support the needs of practitioners regarding 3D geological modelling and uncertainty quantification in the field, in particular from the mining industry, we propose a Python package called loopUI-0.1 that provides a set of local and global indicators to measure uncertainty and features dissimilarities among an ensemble of voxet models. Results are presented of a survey launched among practitioners in the mineral industry, enquiring about their modelling and uncertainty quantification practice and needs. It reveals that practitioners acknowledge the importance of uncertainty quantification even if they do not perform it. A total of four main factors preventing practitioners performing uncertainty quantification were identified: a lack of data uncertainty quantification, (computing) time requirement to generate one model, poor tracking of assumptions and interpretations and relative complexity of uncertainty quantification. The paper reviews and proposes solutions to alleviate these issues. Elements of an answer to these problems are already provided in the special issue hosting this paper and more are expected to come.

Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing

“…Then each row of M S is summed, and the discriminant matrix is set to M D in Formula (10) which derived from Formula (5).…”

“…Three-dimensional GIS modeling is an innovative technique that entails the construction of a model with geospatial data in a virtual 3D space using 3D software or development tools [2,3]. The application of 3D GIS in geological research is essential for geotechnical engineering and exploration work [4,5]. Geologists can use 3D visual models to display geological structures and visualize and analyze geological data effectively [6][7][8].…”

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

“…Although the existing implicit methods can generate various models by perturbing the inputs to characterize uncertainties, they might not explore a broad range of possible geological patterns and structural relationships in nature through a single model suit for stochastic simulation (Jessell et al, 2022). Working on the automation of modeling workflow, our CNN is beneficial for a flexible interpretation of aleatory and epistemic uncertainties (Pirot et al, 2022) by generating diverse modeling realizations instead of one best realization due to its high computational efficiency.…”