Arc magmatic evolution and porphyry copper deposit formation under compressional regime: A geochemical perspective from the Toquepala arc in Southern Peru

Chen, Nian; Mao, Jingwen; Zhang, Zhaochong; Duan, Zheng; Santos, Alan Diego dos; Li, Hongying

doi:10.1016/j.earscirev.2023.104383

Cited by 3 publications

(1 citation statement)

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“…Through the application of big data analytics and methods, scientists have achieved significant advances in geochemical data analysis research. One such achievement includes the accurate classification of lithology [5], as well as distinguishing the rock's tectonic environment [6][7][8], mineral classification [9][10][11], the genesis of ore deposits [12][13][14], and mineral prospectivity mapping [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Review on Global Cooperation Network in the Interdisciplinary Research of Geochemistry Combined with Artificial Intelligence

Zhang,

Zhou,

et al. 2023

Minerals

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With the rapid development of modern geochemical analysis techniques, massive volumes of data are being generated from various sources and forms, and geochemical data acquisition and analysis have become important tools for studying geochemical processes and environmental changes. However, geochemical data have high-dimensional, nonlinear characteristics, and traditional geochemical data analysis methods have struggled to meet the demands of modern science. Nowadays, the development of big data and artificial intelligence technologies has provided new ideas and methods for geochemical data analysis. However, geochemical research involves numerous fields such as petrology, ore deposit, mineralogy, and others, each with its specific research methods and objectives, making it difficult to strike a balance between depth and breadth of investigation. Additionally, due to limitations in data sources and collection methods, existing studies often focus on a specific discipline or issue, lacking a comprehensive understanding of the bigger picture and foresight for the future. To assist geochemists in identifying research hotspots in the field and exploring solutions to the aforementioned issues, this article comprehensively reviews related studies in recent years, elaborates on the necessity and challenges of combining geochemistry and artificial intelligence, and analyzes the characteristics and research hotspots of the global collaboration network in this field. The study reveals that the investigation into artificial intelligence techniques to address geochemical issues is progressing swiftly. Joint research papers serve as the primary means of contact within a worldwide collaborative network. The primary areas of focus in the ongoing research on the integration of geochemistry and artificial intelligence include methodologies for analyzing geochemical data, environmental modifications, and mineral prospectivity mapping. Geochemical data analysis is currently a significant focus of research, encompassing a range of methods including machine learning and deep learning. Predicting mineral resources for deep space, deep Earth, and deep sea is also a pressing topic in contemporary research. This paper explores the factors driving research interest and future trends, identifies current research challenges, and considers opportunities for future research.

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