Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Huang, Jixian; Mao, Xiaoming; Chen, Jin; Deng, Hao; Dick, Jeffrey M.; Liu, Zhankun

doi:10.1007/s11053-019-09560-y

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…It is a local regression model and it estimates local parameters through a data-borrowing scheme. It was originally proposed for 2D space, and Huang et al (2020) extended it to 3D space. Given n dependent variables Y ¼ fy 1 ; y 2 ; _ s; y n g and m explanatory variables…”

Section: Basics Of Gwr In 3d Spacementioning

confidence: 99%

“…The spatial stationary index was introduced to measure the significance of geographical variability (Brunsdon et al, 1996(Brunsdon et al, , 2002Huang et al, 2020); values of > 1 mean non-stationary. Table 6 shows the spatial stationary index values of the parameter estimates, indicating that the relationships between gold grade and the seven ore-controlling factors were not uniform across space and, therefore, the relationships were non-stationary.…”

Section: Spatial Non-stationarity Detectionmentioning

confidence: 99%

“…However, these applications are limited to 2D space. As the depth dimension of real geological space should not be ignored, Huang et al (2020) expanded the GWR to 3D geological space.…”

Section: Introductionmentioning

confidence: 99%

“…Carranza & Hale, 2002;Mao et al, 2016), and, further, this association is spatially anisotropic. While most of existing work (Huang et al, 2020;Tobler, 1979) uses the distance of the homogeneous space to measure nearness, the influence of directionality is not considered in GWR. Therefore, carefully considering the directionality of factors is essential for exploring the non-stationary relationships in GWR.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Spatial non-stationarity is a common geological phenomenon, and the formation of orebodies is a typical non-stationary process. Therefore, a quantitative study of the non-stationary relationships between mineralization and its controlling factors in 3D space is of great significance for metallogenic prediction. Geographically weighted regression (GWR) is an effective method for exploring spatial non-stationarity by measuring the nearness between factors in the data. However, non-stationarity is affected not only by distances but also by factors related to direction. Traditional GWR cannot address the non-stationarity that arises from differences in direction. To address this issue, we propose an improved GWR method to characterize the directional characteristics of non-stationary relationships by introducing a direction weight to the GWR. The anisotropic influence of factors can be obtained by comparing the performance of models with different weights on direction terms. A case study of the Xiadian and Dayingezhuang gold deposits, Jiaodong Peninsula, Eastern China was carried out to verify the anisotropic nature of ore-controlling factors. First, multicollinearity and OLS (ordinary least squares) diagnosis for the variables were performed and the necessity of the non-stationarity exploration was demonstrated. Second, GWR was applied to explore the spatial non-stationarity of the relationships among the variables by comparing the global R 2 value with that of OLS, evaluating the local R 2 , values testing the tstatistic values, analyzing and comparing the spatial autocorrelation of residuals with that of OLS, and calculating the spatial stationary index of the parameter estimates of explanatory variables. Third, the improved GWR method was applied, and the directional characteristics of the non-stationary relationship were analyzed. Finally, the anisotropic influence of the controlling factors on mineralization was validated by comparing the performance of the improved GWR model with different bandwidths and different kernels, and the importance of the direction of the fault zone to mineralization was further verified.

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Section: Basics Of Gwr In 3d Spacementioning

confidence: 99%

Section: Spatial Non-stationarity Detectionmentioning

confidence: 99%

“…However, these applications are limited to 2D space. As the depth dimension of real geological space should not be ignored, Huang et al (2020) expanded the GWR to 3D geological space.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Blind Source Separation of Spectrally Filtered Geochemical Signals to Recognize Multi-depth Ore-Related Enrichment Patterns

Esmaeiloghli,

Tabatabaei,

Hosseini

et al. 2023

Math Geosci

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Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Cited by 14 publications

References 41 publications

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

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

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

Blind Source Separation of Spectrally Filtered Geochemical Signals to Recognize Multi-depth Ore-Related Enrichment Patterns

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