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Keywords = hidden fluorite ore

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21 pages, 12899 KB  
Article
Location Prediction Study of Fluorite Ore in Shallow Cover Area: Evidence from Integrated Geophysical Surveys
by Liu Cheng, Li Han, Yang Kai, Gao Yongbao, Tang Weidong and Yao Chuan
Minerals 2024, 14(8), 838; https://doi.org/10.3390/min14080838 - 19 Aug 2024
Viewed by 1297
Abstract
The Beishan region is a vital fluorite metallogenic belt in northwest China, characterized by favorable geological conditions for fluorite mineralization. However, being located in the Gobi Desert and affected by shallow cover layers, only a few outcrops can be observed on the surface. [...] Read more.
The Beishan region is a vital fluorite metallogenic belt in northwest China, characterized by favorable geological conditions for fluorite mineralization. However, being located in the Gobi Desert and affected by shallow cover layers, only a few outcrops can be observed on the surface. Therefore, comprehensive geophysical research is necessary to locate and predict regional metallogenic potential and the spatial distribution of veins beneath the cover. This study conducted a combination of ground magnetic method (GM), induced polarization (IP) surveys, portable gamma-ray (PGR), portable X-ray fluorescence analyses (PXRF), and audio Magnetotelluric (AMT) to conduct comprehensive exploration. The IP and GM effectively identified concealed ore-bearing space distributions and ground PGR- and PXRF-constrained mineralization anomalies, while AMT surveys constructed deep electrical structure models for ore deposits. This approach delineated concealed fluorite deposit locations as well as potential magmatic–hydrothermal migration pathways. Engineering verification confirmed the effectiveness of this method combination. This study established a comprehensive geological–geophysical positioning prediction technique that can serve as a reference for locating and predicting fluorite deposits in shallow-covered areas within the Gobi Desert. Full article
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16 pages, 2644 KB  
Article
Crystal-Site-Based Artificial Neural Networks for Material Classification
by Juan I. Gómez-Peralta, Nidia G. García-Peña and Xim Bokhimi
Crystals 2021, 11(9), 1039; https://doi.org/10.3390/cryst11091039 - 29 Aug 2021
Cited by 5 | Viewed by 5876
Abstract
In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the [...] Read more.
In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures. Full article
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