Prospects of Artificial Intelligence Application in the High-Quality Development of the Geological Exploration Industry
- DOI
- 10.2991/978-94-6463-260-6_12How to use a DOI?
- Keywords
- high-quality development; geological exploration industry; artificial intelligence; prospects analysis
- Abstract
Artificial intelligence (AI) plays a pivotal role in facilitating the high-quality development of the geological exploration industry. Its applications span across diverse areas, including data analysis and interpretation, automated exploration processes, resource optimization management, risk assessment and management, intelligent resource development, and knowledge sharing and collaboration. By leveraging AI in these domains, the geological exploration industry can progress towards achieving high-quality outcomes. AI enables more accurate guidance for exploration activities, enhances efficiency, improves the quality of results, reduces costs, predicts risks, optimizes resource development, and accelerates knowledge dissemination and application. These crucial roles provided by AI contribute significantly to the sustainable development of the geological exploration industry.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yao Xia AU - Yan Feng PY - 2023 DA - 2023/09/28 TI - Prospects of Artificial Intelligence Application in the High-Quality Development of the Geological Exploration Industry BT - Proceedings of the 2023 International Conference on Management Innovation and Economy Development (MIED 2023) PB - Atlantis Press SP - 94 EP - 101 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-260-6_12 DO - 10.2991/978-94-6463-260-6_12 ID - Xia2023 ER -