Three-Dimensional Prospectivity Modeling of Honghai Volcanogenic Massive Sulfide Cu–Zn Deposit, Eastern Tianshan, Northwestern China Using Weights of Evidence and Fuzzy Logic

Tao, Jintao; Yuan, Feng; Zhang, Nannan; Chang, Jinyu

doi:10.1007/s11004-019-09844-2

Cited by 12 publications

(3 citation statements)

References 71 publications

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“…It utilizes its nonlinear learning ability to characterize potential complex geological features by continuously training models and fitting parameters. In recent years, many scholars have begun to try to carry out 3DMPM research, including using the evidence weight model, the logistic regression model, the random forest model, and the artificial neural network model [28][29][30][31]. The above methods have shown good research potential in the field of 3DMPM.…”

Section: D Mineral Prospectivity Modelingmentioning

confidence: 99%

Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China

Meng

Chen

et al. 2022

Minerals

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The Middle–Lower Yangtze River Metallogenic Belt is an important copper and iron polymetallic metallogenic belt in China. Today’s economic development is inseparable from the support of metal mineral resources. With the continuous exploitation of shallow and easily identifiable mines in China, the prospecting work of deep and hidden mines is very important. Mineral prospectivity modeling (MPM) is an important means to improve the efficiency of mineral exploration. With the increase in resource demands and exploration difficulty, the traditional 2DMPM is often difficult to use to reflect the information of deep mineral deposits. More large-scale deposits are needed to carry out 3DMPM research. With the rise of artificial intelligence, the combination of machine learning and geological big data has become a hot issue in the field of 3DMPM. In this paper, a case study of 3DMPM is carried out based on the Xuancheng–Magushan area’s actual data. Two machine learning methods, the random forest and the logistic regression, are selected for comparison. The results show that the 3DMPM based on random forest method performs better than the logistic regression method. It can better characterize the corresponding relationship between the geological structure combination and the metallogenic distribution, and the accuracy in the test set reaches 96.63%. This means that the random forest model could provide more effective and accurate support for integrating predictive data during 3DMPM. Finally, five prospecting targets with good metallogenic potential are delineated in the deep area of the Xuancheng–Magushan area for future exploration.

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Section: D Mineral Prospectivity Modelingmentioning

confidence: 99%

Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China

Meng

Chen

et al. 2022

Minerals

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“…(2) In contrast, the mineralization prediction elements are obtained based on the conceptual model of mineralization, the obtained mineralization prediction elements are used as input to the GNB and SVM models, and the two prediction results were compared with the ore-bearing voxels locations determined by Apriori. There are 414 positive and negative samples, of which 207 positive samples are recognized from known drilling data (the 207 positive samples are the same as the data used for Apriori mining), and the negative samples are chosen from ore-free voxels identified in the drill holes and known ore-free places on the surface [29,30]. The fourth step ( 4) is divided into two aspects: in the first aspect, the optimal prediction model between the SVM and GNB models is determined based on the receiver operating characteristic (ROC) [31].…”

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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“…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. Subsequently, they used the weight of evidence method and fuzzy logic to integrate various predictor maps, in order to generate perspective maps.…”

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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Three-Dimensional Prospectivity Modeling of Honghai Volcanogenic Massive Sulfide Cu–Zn Deposit, Eastern Tianshan, Northwestern China Using Weights of Evidence and Fuzzy Logic

Cited by 12 publications

References 71 publications

Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China

Three-Dimensional Mineral Prospectivity Modeling for Delineation of Deep-Seated Skarn-Type Mineralization in Xuancheng–Magushan Area, China

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

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

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