Creating large scale probabilistic boundaries using Gaussian Processes

Ball, Adrian; Silversides, Katherine L.; Chlingaryan, Anna; Melkumyan, Arman

doi:10.1016/j.eswa.2022.116959

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…This knowledge can assist miners with planning and various decision making processes [13], for instance, to prioritize areas of excavation, to develop a mining schedule [7], to optimize the quality of an ore blend in a production plant. Of particular relevance to spatial modeling is that wireframe surfaces can be generated by geo-modeling software [28] [20] [12], or via kriging [9], probabilistic boundary estimation [3], boundary propagation (differential geometry) [14], and other inference techniques [31] to minimize the uncertainty of interpolation at locations where data were previously unavailable. For instance, triangle meshes may be created by applying the marching cubes algorithm [22] to Gaussian process implicit surfaces [8].…”

Section: Introductionmentioning

confidence: 99%

Modelling Orebody Structures: Block Merging Algorithms and Block Model Spatial Restructuring Strategies Given Mesh Surfaces of Geological Boundaries

Leung¹

2020

JOSIS

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This paper describes a framework for capturing geological structures in a 3D block model and improving its spatial fidelity, including the correction of stratigraphic, mineralization, and other types of boundaries, given new mesh surfaces. Using surfaces that represent geological boundaries, the objectives are to identify areas where refinement is needed, increase spatial resolution to minimize surface approximation error, reduce redundancy to increase the compactness of the model and identify the geological domain on a block-by-block basis. These objectives are fulfilled by four system components which perform block-surface overlap detection, spatial structure decomposition, sub-blocks consolidation, and block tagging, respectively. The main contributions are a coordinate-ascent merging algorithm and a flexible architecture for updating the spatial structure of a block model when given multiple surfaces, which emphasizes the ability to selectively retain or modify previously assigned block labels. The techniques employed include block-surface intersection analysis based on the separable axis theorem and ray-tracing for establishing the location of blocks relative to surfaces. To demonstrate the robustness and applicability of the proposed block merging strategy in a more narrow setting, it is used to reduce block fragmentation in an existing model where surfaces are not given and the minimum block size is fixed. To obtain further insight, a systematic comparison with octree subblocking subsequently illustrates the inherent constraints of dyadic hierarchical decomposition and the importance of inter-scale merging. The results show the proposed method produces merged blocks with less extreme aspect ratios and is highly amenable to parallel processing. The overall framework is applicable to orebody modeling given geological boundaries, and 3D segmentation more generally, where there is a need to delineate spatial regions using mesh surfaces within a block model.

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Section: Introductionmentioning

confidence: 99%

Modelling Orebody Structures: Block Merging Algorithms and Block Model Spatial Restructuring Strategies Given Mesh Surfaces of Geological Boundaries

Leung¹

2020

JOSIS

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Addressing Application Challenges with Large-Scale Geological Boundary Modelling

Ball

Zigman²,

Melkumyan

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

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For banded iron formation-hosted deposits accurate boundary modelling is critical to ore-grade estimation. Key to estimation fidelity is the accurate separation of the different domains within the ore body, requiring modelling of the boundaries between domains. This yields both theoretical and application challenges. We present a series of solutions for application challenges that arise when modelling large-scale boundaries employing a composition of Gaussian Process models on exploration and production hole data. We demonstrate these in the banded iron formation-hosted iron ore deposits in the Hamersley Province of Western Australia. We present solutions to several challenges: the inclusion of information derived from a geologist-defined boundary estimate to incorporate domain knowledge in data sparse regions, the incorporation of unassayed production holes that are implicitly defined as waste to augment production hole assay data, and a more holistic method of defining regional bounds and spatial rotations for Gaussian Process modelling of local spaces. Solution are evaluated against a range of metrics to show performance improvements over the manually performed estimation by an expert geologist of the boundaries delineating the ore body domains. Reconcilliation scores are used for evaluating the quality of predicted domain boundaries against measured production data. The predicted and in situ surfaces are also qualitatively evaluated against production data to ensure that the models were evaluated to be geologically sound by an expert in the field. In particular, better fidelity is shown when separating mineralised and non-mineralised ore, consequently improving the estimation of the ore-grades present in the mine site.

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Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide

Min,

Kim,

Kim

et al. 2023

Landslides

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Creating large scale probabilistic boundaries using Gaussian Processes

Cited by 3 publications

References 22 publications

Modelling Orebody Structures: Block Merging Algorithms and Block Model Spatial Restructuring Strategies Given Mesh Surfaces of Geological Boundaries

Modelling Orebody Structures: Block Merging Algorithms and Block Model Spatial Restructuring Strategies Given Mesh Surfaces of Geological Boundaries

Addressing Application Challenges with Large-Scale Geological Boundary Modelling

Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide

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