Preface: State of the art in mineral exploration

Alcalde, Juan; Carbonell, Ramón; Pospiech, Solveig; Iglesia, Alba Gil de la; Bullock, Liam A.; Tornos, Fernando

doi:10.5194/se-13-1161-2022

Solid Earth

2022

DOI: 10.5194/se-13-1161-2022

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Preface: State of the art in mineral exploration

Juan Alcalde

Ramón Carbonell

Solveig Pospiech

et al.

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Cited by 3 publications

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“…presents a summary of four different research article explaining investigating current state of the art applications of artificial intelligence in mining. The first paper byAlcalde et al (2022) provides an overview of the current state of the art in mineral exploration. The paper highlights the use of advanced technologies and interdisciplinary approaches to identify new mineral deposits in a socially and environmentally responsible manner.…”

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Exploring the Potential of Artificial Intelligence and Machine Learning in Mineral Exploration: A Review Article

2023

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Mineral exploration is a complex and challenging process, requiring vast amounts of data to be analyzed to make informed decisions. Traditional methods of mineral exploration are time-consuming, expensive, and often yield low success rates. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) presents an opportunity to revolutionize the mining industry. This review article investigates the current stateof-the-art applications of AI and ML in mineral exploration, evaluates their effectiveness and limitations, and identifies the potential benefits and challenges of their adoption. The study highlights that AI and ML techniques can significantly increase the efficiency and success rate of mining projects. Various AI and ML algorithms such as Neural Networks, Decision Trees, and Random Forests are being used for mineral exploration. These techniques help in identifying patterns and correlations in the vast amount of data, reducing the time and cost involved in mineral exploration. The study also identifies potential limitations such as the need for high-quality data, the lack of interpretability of the results, and the ethical considerations that need to be addressed when using AI and ML in mining. The findings of this study have significant implications for the mining industry. The adoption of AI and ML techniques in mineral exploration can lead to increased profitability, reduced costs, and improved environmental and social impacts. The study provides recommendations for future research and development of AI and ML techniques in mineral exploration. In conclusion, the potential of AI and ML in mineral exploration is immense, and their adoption could lead to a paradigm shift in the mining industry.

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Exploring the Potential of Artificial Intelligence and Machine Learning in Mineral Exploration: A Review Article

2023

IRJMETS

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Comparative analysis of divergence of tipper imaging with natural and orthogonal electric sources

Yu,

Wang,

Liu

et al. 2024

Journal of Electromagnetic Waves and Applications

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Ambient noise surface-wave imaging in a hardrock environment: implications for mineral exploration

Wilczynski,

Kaslilar,

Malehmir

et al. 2024

Geophysical Journal International

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Summary The advancement of seismic methods is vital for mineral exploration in the ongoing energy transition. In this study, we investigate the application of ambient noise seismic interferometry and surface-wave analysis to characterize the subsurface in a mineral exploration context. We then confirm the results of the passive seismic investigation through an active source experiment. We collected ambient noise data using a 2D seismic line initially deployed for an active source reflection seismic study. By cross-correlating the signals, we retrieved the surface waves and constructed a 2D shear-wave velocity profile using conventional surface-wave analysis. We utilized the active source data to establish initial assumptions about the surveyed medium and then validated the passive seismic experiment. The passive seismic results are concordant with the active source results and allow for the interpretation of geological contacts and fault zones. Our work demonstrates the potential of passive seismic methods for investigating local tectonic settings and their role in hardrock mineral exploration.

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Preface: State of the art in mineral exploration

Cited by 3 publications

References 49 publications

Exploring the Potential of Artificial Intelligence and Machine Learning in Mineral Exploration: A Review Article

Exploring the Potential of Artificial Intelligence and Machine Learning in Mineral Exploration: A Review Article

Comparative analysis of divergence of tipper imaging with natural and orthogonal electric sources

Ambient noise surface-wave imaging in a hardrock environment: implications for mineral exploration

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