Crucial Geochemical Signal Identification for Cu-Fertile Magmas in Paleo-Tethyan Arc Based on Machine Learning

Zhang, Zhen-Jie; Zhou, Yuanzhi; Zhang, Peng

doi:10.1007/s11004-023-10055-z

Cited by 6 publications

(2 citation statements)

References 78 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overarching objective is to maximize the preservation of trend and pattern information [97]. This method has been tested and proved to be effective in constructing discriminant models for investigating mineral geochemistry [98].…”

Section: Principal Component Analysismentioning

confidence: 99%

See 1 more Smart Citation

Application of Machine Learning to Research on Trace Elemental Characteristics of Metal Sulfides in Se-Te Bearing Deposits

Zhang,

Wang,

et al. 2024

Minerals

View full text Add to dashboard Cite

This study focuses on exploring the indication and importance of selenium (Se) and tellurium (Te) in distinguishing different genetic types of ore deposits. Traditional views suggest that dispersed elements are unable to form independent deposits, but are hosted within deposits of other elements as associated elements. Based on this, the study collected trace elemental data of pyrite, sphalerite, and chalcopyrite in various types of Se-Te bearing deposits. The optimal end-elements for distinguishing different genetic type deposits were recognized by principal component analysis (PCA) and the silhouette coefficient method, and discriminant diagrams were drawn. However, support vector machine (SVM) calculation of the decision boundary shows low accuracy, revealing the limitations in binary discriminant visualization for ore deposit type discrimination. Consequently, two machine learning algorithms, random forest (RF) and SVM, were used to construct ore genetic type classification models on the basis of trace elemental data for the three types of metal sulfides. The results indicate that the RF classification model for pyrite exhibits the best performance, achieving an accuracy of 94.5% and avoiding overfitting errors. In detail, according to the feature importance analysis, Se exhibits higher Shapley Additive Explanations (SHAP) values in volcanogenic massive sulfide (VMS) and epithermal deposits, especially the latter, where Se is the most crucial distinguishing element. By comparison, Te shows a significant contribution to distinguishing Carlin-type deposits. Conversely, in porphyry- and skarn-type deposits, the contributions of Se and Te were relatively lower. In conclusion, the application of machine learning methods provides a novel approach for ore genetic type classification and discrimination research, enabling more accurate identification of ore genetic types and contributing to the exploration and development of mineral resources.

show abstract

Section: Principal Component Analysismentioning

confidence: 99%

“…The SVM is one of the most commonly employed algorithms in ore exploration prediction, and proves highly effective in mineral exploration mapping, lithology classification, alteration phase identification, and geochemical discrimination [98,102,105,106].…”

Section: Support Vector Machinementioning

confidence: 99%

Application of Machine Learning to Research on Trace Elemental Characteristics of Metal Sulfides in Se-Te Bearing Deposits

Zhang,

Wang,

et al. 2024

Minerals

View full text Add to dashboard Cite

show abstract

Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at Wulonggou, Qinghai Province (Western China)

Yan,

Zhao,

Xue

et al. 2024

Nat Resour Res

View full text Add to dashboard Cite

Prospectivity mapping based on deep learning typically requires substantial amounts of geological feature information from known mineral deposits. Due to the limited spatial distribution of ore deposits, the training of predictive models is often hampered by insufficient positive samples. Meanwhile, data-driven mineral prospectivity mapping often overlooks domain knowledge and expert experience, leading to poor interpretability of predictive results. To address this problem, we employed the Gaussian mixture model (GMM) for spatial feature classification to expand the number of positive samples. The approach integrated the embedding of geological map knowledge graphs with geological exploration data to enhance the knowledge constraints of the prospecting model, which enabled the integration of knowledge with data. Considering the complex spatial structure of geological elements, a bi-branch utilizing the 1-dimensional convolutional neural network (CNN1D) and graph convolutional network (GCN) was used to extract geological spatial features for model training and prediction. To validate the effectiveness of the method, a gold mineralization prediction study was conducted in the Wulonggou area (Qinghai province, western China). The results indicate that, when the number of GMM spatial feature classifications was 17, the positive-to-negative sample ratio was optimal, and the embedding of the knowledge graph controlled the prediction area distribution effectively, which demonstrated strong consistency between the prospecting area and the known mineral deposits. Compared with the predictions by CNN1D, the fused prediction model of CNN1D and GCN yielded higher accuracy. Our model identified 11 classes of mineralization potential areas and provides geological interpretations for different prediction categories.

show abstract

Data mining for geochemical signatures of volcanic-type uranium mineralization, Duolun-Guyuan prospect, North China

Zhang,

Wang,

Wang

et al. 2024

Journal of Geochemical Exploration

View full text Add to dashboard Cite

Crucial Geochemical Signal Identification for Cu-Fertile Magmas in Paleo-Tethyan Arc Based on Machine Learning

Cited by 6 publications

References 78 publications

Application of Machine Learning to Research on Trace Elemental Characteristics of Metal Sulfides in Se-Te Bearing Deposits

Application of Machine Learning to Research on Trace Elemental Characteristics of Metal Sulfides in Se-Te Bearing Deposits

Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at Wulonggou, Qinghai Province (Western China)

Data mining for geochemical signatures of volcanic-type uranium mineralization, Duolun-Guyuan prospect, North China

Contact Info

Product

Resources

About