Grade and Tonnage Uncertainty Analysis of an African Copper Deposit Using Multiple-Point Geostatistics and Sequential Gaussian Simulation

Paithankar, Amol; Chatterjee, Snehamoy

doi:10.1007/s11053-017-9364-1

Cited by 32 publications

(7 citation statements)

References 32 publications

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“…Recent developments have also explored TI-free approaches [5,6]. MPS algorithms have found applications across various fields, including reservoir geology [7,8], mineral deposits modeling [9,10], seismic inversion [11], porosity modeling [12], hydrology [13], groundwater modeling [14][15][16], climate modeling [17][18][19], and remote sensing [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Asadi,

Chatterjee

2024

Geosciences

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Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.

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Section: Introductionmentioning

confidence: 99%

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Asadi,

Chatterjee

2024

Geosciences

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“…The simulation result can reflect the spatial variation of the target attribute more realistically. Recently, geostatistical simulation methods have been widely used in the quantitative uncertainty of the geological field [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Relevant simulation algorithms include pixel-based simulations, such as the extended normal equations simulation (ENESIM) algorithm [20], the single normal equation simulation (SNESIM) algorithm [25], and direct sampling (DS) algorithm [26,27]; pattern-based simulations, such as the simulation of patterns (SIMPAT) algorithm [28]; the filter-based pattern simulation (FILTERSIM) algorithm [29], and the cross-correlation-based simulation (CCSIM) algorithm [30]. These methods have been effectively applied in reservoir simulation, hydrogeological modeling, porous media reconstruction, and other geoscience fields [31][32][33], but there are few applications related to geochemical exploration [17,34,35]. The main reason for this is that an appropriate training image (TI) is difficult to obtain through the complex geochemical element spatial distribution.…”

Section: Introductionmentioning

confidence: 99%

Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

Liu

Guo

et al. 2021

Minerals

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The smoothing effect of data interpolation could cause useful information loss in geochemical mapping, and the uncertainty assessment of geochemical anomaly could help to extract reasonable anomalies. In this paper, multiple-point geostatistical simulation and local singularity analysis (LSA) are proposed to identify regional geochemical anomalies and potential mineral resources areas. Taking Cu geochemical data in the Mila Mountain Region, southern Tibet, as an example, several conclusions were obtained: (1) geochemical mapping based on the direct sampling (DS) algorithm of multiple-point geostatistics can avoid the smoothing effect through geochemical pattern simulation; (2) 200 realizations generated by the direct sampling simulation reflect the uncertainty of an unsampled value, and the geochemical anomaly of each realization can be extracted by local singularity analysis, which shows geochemical anomaly uncertainty; (3) the singularity-quantile (S-Q) analysis method was used to determine the separation thresholds of E-type α, and uncertainty analysis was carried out on the copper anomaly to obtain the anomaly probability map, which should be more reasonable than the interpolation-based geochemical map for geochemical anomaly identification. According to the anomaly probability and favorable geological conditions in the study area, several potential mineral resource targets were preliminarily delineated to provide direction for subsequent mineral exploration.

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“…They considered dynamic updatability as one of the metrics to assess 3D geological modelling methods. Paithankar and Chatterjee [14] applied a multi-point geostatistical method and sequential Gaussian simulation to generate multiple equiprobable models of a selected deposit in Africa. Tao et al [15] created a 3D geological model based on geological maps, geological plans, cross sections and boreholes.…”

Section: Introductionmentioning

confidence: 99%

Spatial Modelling of Kaolin Deposit Demonstrated on the Jimlíkov-East Deposit, Karlovy Vary, Czech Republic

Jarošová

Staněk

2021

IJGI

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The present study is focused on spatial modelling of a kaolin deposit in Karlovy Vary, Czech Republic, and the methodical procedure of development, evaluation and visualization of a 3D model are described step by step. The implementation of this methodology is performed in Visual Studio 2019 with use of the Surfer and Voxler objects from Golden Software. This methodology combined with the newly developed software (Kaolin_A and Kaolin_Viz programs) allow a user to create a variant dynamic model for the same or similar types of deposits. It enables a quick update of the model when changing the input data, based on the new mining exploration or when changing the modelling parameters. The presented approach leads to a more advanced evaluation of deposits, including various estimates of reserves according to pre-specified usability conditions. The efficiency of the developed methodology and the software for the evaluation of the deposit are demonstrated on the kaolin deposit Jimlíkov-East, located near the village Jimlíkov about 5 km west of Karlovy Vary in the Czech Republic.

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Grade and Tonnage Uncertainty Analysis of an African Copper Deposit Using Multiple-Point Geostatistics and Sequential Gaussian Simulation

Cited by 32 publications

References 32 publications

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

Spatial Modelling of Kaolin Deposit Demonstrated on the Jimlíkov-East Deposit, Karlovy Vary, Czech Republic

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