Decision-making within geochemical exploration data based on spatial uncertainty – A new insight and a futuristic review

Sadeghi, Behnam; Cohen, David R.

doi:10.1016/j.oregeorev.2023.105660

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…Although the field of geochemical artificial intelligence has made many advances, there are still many challenges and problems that need further research and resolution. For example, how to improve the generalization and robustness of models, how to handle imbalanced data distribution, and how to apply advanced algorithms to practical scenarios are issues that need further research and resolution in this field [4,45,[61][62][63][64]. In addition, compared with other fields, research achievements and applications in geochemical artificial intelligence are relatively limited, requiring more innovation and breakthroughs.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…While some aspects of uncertainty related to geochemical mapping have been explored in previous studies (e.g., Costa & Koppe, 1999; Liu & Carranza, 2022; Liu et al., 2019; Sadeghi & Cohen, 2021, 2023; Wang & Zuo, 2018, 2022, 2023; Wang et al., 2022), to the best of our knowledge, there is currently no comprehensive review on this topic in the relevant literature. Also, note that we primarily focus on regional‐scale geochemical mapping, the most common scale for this type of study, although it can be carried out at various scales and different uncertainties may arise (e.g., Carranza et al., 2019; Chiprés et al., 2008; Demetriades et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification in Geochemical Mapping: A Review and Recommendations

Wang,

Zuo

2024

Geochem Geophys Geosyst

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Geochemical mapping is a crucial tool that can provide valuable insights for a wide range of applications, including mineral resources prospecting, environmental impact assessment, geological process understanding, and climate change research. Despite its significance, geochemical mapping requires spatial modeling based on sparse, heterogeneous, and potentially inaccurate data sets. Moreover, the underlying geological processes are often imperfectly understood. Therefore, uncertainty quantification (UQ) is vital in geochemical mapping to ensure accurate and reliable results, ultimately facilitating well‐informed decision‐making. In this contribution, we distinguish two primary types of uncertainties: systemic and stochastic. We identify the key sources of uncertainties in geochemical mapping and review the techniques that have been employed or hold potential for uncertainty quantification, communication, visualization, and sensitivity analysis. This contribution also illustrates the general procedure of UQ in geochemical mapping by a case study of mapping geochemical anomalies associated with gold mineralization in northwestern Sichuan Province, China. We also explore potential strategies for mitigating the critical uncertainties, such as gathering more geochemical data, developing more effective models, enhancing our understanding of the geochemical dispersion process, or leveraging other thematic information or knowledge. Future research should prioritize addressing underexplored uncertainties and implementing more practical applications to validate the UQ procedure in geochemical mapping.

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Computing with Words to Assess the Perceived Quality of IT Products and Projects

Peña Abreu,

Mejias Cruz,

López Valerio

et al. 2024

Studies in Computational Intelligence

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Decision-making within geochemical exploration data based on spatial uncertainty – A new insight and a futuristic review

Cited by 7 publications

References 49 publications

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

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

Uncertainty Quantification in Geochemical Mapping: A Review and Recommendations

Computing with Words to Assess the Perceived Quality of IT Products and Projects

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