An Improved GWR Approach for Exploring the Anisotropic Influence of Ore-Controlling Factors on Mineralization in 3D Space

Huang, Jixian; Mao, Xiaoming; Deng, Hao; Liu, Zhankun; Chen, Jin; Xiao, Keyan

doi:10.1007/s11053-021-09954-x

Cited by 8 publications

(1 citation statement)

References 36 publications

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“…The mineralization prediction is one of the important elements of the mineral census exploration process [5]. The relationship between this process and the multiple sources of integrated mineralization information is complex, nonlinear and non-stationarity [6][7][8][9]. This also determines the limitations of traditional linear mathematical and statistical modeling methods in their application for mineralization prediction.…”

Section: Introductionmentioning

confidence: 99%

Apriori Algorithm-Based Three-Dimensional Mineral Prospectivity Mapping—An Example from Meiling South Area, Xinjiang, China

Chang,

Zhang,

Zhou

et al. 2023

Minerals

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Mineral Prospectivity Mapping (MPM) is shifting toward intelligent deep mineralization searches in the era of big data and the increasing difficulties of surface deposit detection. Comparative analysis of two forms of mineralization prediction based on the Apriori algorithm was performed in the Meiling South mining area in the eastern Hami region of Xinjiang, China. In comparison 1, we use the Apriori algorithm to mine ore-forming information and determine the ore-forming voxel positions based on spatial distance and angle analysis. Then, we compare the ore-forming voxel positions determined by Apriori with the ore-forming voxel positions predicted by the mathematical model based on the conceptual model of mineralization, and these mathematical models include Gaussian Naive Bayesian (GNB) and Support Vector Machine (SVM). In comparison 2, the optimal prediction model is SVM, which is trained using the elements of mineralization prediction determined by the conceptual model of mineralization. Then, two sets of new elements of mineralization prediction are extracted from the original elements of mineralization prediction using the Apriori and Chi-square methods and then input into the SVM model for training. After we obtain the mineralization prediction results, we compare them with the original mineralization prediction results. The preceding comparison produced the following results. (1) Using the Apriori algorithm, the distribution characteristics of the high and low-grade ore bodies and the association rules between ore-bearing information were determined. (2) The prediction results of the GNB and SVM models displayed corresponding trends on the high and low-grade ore-bearing voxels identified by Apriori, which matched the rules mined by Apriori. (3) In comparison to the mineralization prediction elements screened by Chi-square and the original mineralization prediction elements based on the conceptual model of mineralization, the elements of mineralization prediction chosen based on Apriori have the best prediction effect in SVM when tested in new drill holes. Based on the mineralization prediction elements screened by Apriori, the number of accurate ore-bearing voxels (prediction probability greater than 0.5) predicted by the SVM model is 6, 5, and 1 in drill holes V1, V2, and V3, respectively. The collective results demonstrated that Apriori is explicit, intuitive, and interpretable for mineralization prediction and has a certain reference value for refining the determination of mineralization prediction elements and discovering mineralization mechanisms and laws.

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