At present, the world's century-old dramatic changes are accelerating, a new scientific and technological revolution and industrial transformation are flourishing, the world's major economies continue to strengthen their strategic deployment strategies for the exploitation of key mineral resources, mining technology equipment becomes a key variable in the international mining cooperation contest, and frontier technology becomes an important strategic position for major countries to compete. Since 2023, technologies such as artificial intelligence (ai) are having an overall impact on the global model of mineral resource development as new quality productivity and have begun to yield results in practical applications。
01
Definition and application of ai
At present, artificial intelligence, known as ai, is considered the next “super-window” of science and technology innovation. Ai focuses on research and development of intelligent theories, methods, technologies and systems applications for modelling and extension of people, including robotics, language and image recognition, computer visual sensory systems, natural language management, machine learning and professional analysis systems. The essence of ai is to provide statistical analysis of big data through large data models, to summarize, remember and summarize from the data, and to respond in a manner similar to human intelligence based on practical applications. At present, ai has been widely applied in the areas of natural language processing, image recognition and computer visualization, intellectual software and hardware optimization, industrial production, decision-making management, trade finance, medicine and medicine. Ai has automated and intelligent production processes through machine in-depth learning, industrial internet, data sharing, modelling, etc. So as to continuously improve production efficiency, reduce costs, optimize processes, improve product quality and promote high-quality development in all areas。
02
Principles of ai's application in mining
Mining exploration is in itself a professional effort to collect data and knowledge-driven analyses. It is a process of collecting data, analysing data with knowledge, leading to mining survey findings, building geological models of mineral deposits and optimizing mining programmes. In terms of data collection, it is the collection of information through a variety of geological, geophysical, geochemical and remote sensing techniques, the collection of geo-information data through methods such as foot surveys, scavengers, drilling, etc., which provides the basis for data analysis in order to locate mines; the knowledge-driven approach is the processing of geological data and impacts of various types, based on the experience of experts in the field of geological mining, and the establishment of mine-seeking models to guide exploration directions in the analysis of the location, size, probability of discovery, etc. Of deposits, etc., as well as through the presentation of maps. Ai specializes in processing large amounts of data, deciphering geological surveys, satellite images and historical exploration data, rapidly building visualization of geological mine models, and quickly determining patterns, anomalies and potential deposits that may not be identified in traditional exploration methods through machine-learning models such as neural networks, thus achieving mine breakthroughs. In the area of mining, ai's role is to replace manual labour and is currently seeking practical applications in mining surveys, geological data and image processing, three-dimensional geological modelling, disaster warning, etc。
03
Ai applications in mining are beginning to yield results and are ready for the future
First, ai is looking for initial successful applications in mining surveys, mine production, data image processing, geohazard warning, etc. In the search for mining, the ai system can identify geological anomalies and mineralization characteristics and predict the location and size of underground deposits, such as the mining company kobold m, which invested in bill gates and jeff bezos in 2024Using ai technology, etals discovered high reserves and high-grade mingomba large copper deposits in zambia. In the area of mine production, ai-based sorting systems enable real-time rapid identification of valuable minerals in waste stones and increase the rate of recovery and utilization of mineral resources. For example, the norwegian company tauran has developed a mineral selection system that applies to different mineral species using sensors and artificial intelligence, and bhp has worked with microsoft to improve copper recovery in the escondida copper mine through ai and machine learning. At the same time, ai technology can achieve comprehensive monitoring of the mine production environment, detecting and addressing insecurity in a timely manner through integrated video surveillance, smart identification algorithms and large data analysis platforms. In the area of data image processing, ai can quickly and accurately complete such tasks as data disaggregation, data aggregation, data characterization, such as the three-dimensional inversion of geophysical data, automatic geo-filling of high-resolution remote sensing images and three-dimensional geological modelling of mines. In the area of early warning of geological disasters, ai, combined with remote sensing technology and physical networking equipment, can monitor changes in the geological environment, such as landslides, earthquakes and groundwater table fluctuations, in real time. Through rapid identification and intelligence analysis of abnormal data, it can predict more accurately the probability and extent of natural disasters, such as earthquakes, landslides and landslides。
Secondly, there is a growing global enterprise using ai technology for mineral exploration, with great development prospects. In a new wave of technological revolution and industrial change, the mining industry, as one of the oldest traditional industries in humankind, is undergoing the most radical change ever. Mineral exploration is emerging as the most innovative and explosive area, while mining start-ups are the most promising breakout group. According to the latest statistics from the silicon valley technology review database, almost 100 start-ups worldwide have used ai technology for mineral surveys, including ver ai, earth ai, plotlogic, geolog ai and ideon technologies. Our scholars have also been actively exploring this area, where several teams, such as the institute of mineral resources of the chinese academy of geology, the chinese university of geology, the university of china, the university of nakayama and the university of nakayama, have made significant contributions, with many theoretical and technical innovations, the establishment of a model for finding mines — a three-dimensional model — and a theoretical method for predicting mineral resources in the depths of quantitative data, as well as breakthroughs in practical applications for finding mines。
Thirdly, major global economies attach great importance to ai technology development. In recent years, major global economies have placed high priority on ai's development in various areas, including mining, and have continued to increase their financial investment. In may 2024, the united states launched the prelude to ai legislation, and its ai working group released " advancing u. S. Innovation in the field of ai: a senate ai policy road map " , which proposes to strengthen ai technology research and development and governance and requires the united states government to invest $32 billion annually in research on non-defence artificial intelligence innovation, reflecting the high priority given to the ai development landscape. In april 2024, the government of canada announced an investment of approximately $1. 8 billion to promote the development of the ai industry, mainly for building scientific and technological infrastructure, upgrading computing capabilities and providing resources for ai researchers, start-ups and related companies. In august 2022, the ministry of science and technology published a circular on supporting the construction of a new generation of artificial smart demonstration applications, which explicitly identified smart mines as the first 10 priority ai demonstration applications. In april 2024, seven departments, including the national monitoring and monitoring directorate for mines, jointly issued guidance on in-depth promotion of the smart construction of mines for their safe development, proposing algorithmic optimization and modelling of large-scale models for accelerating the intellectualization of mines and strengthening the practical level of ai production in mines。
04
Ai technology has a clear advantage over traditional mining exploration and exploitation methods
The first is the effective improvement in the accuracy and success of mineral exploration. Mining exploration has become more complex and more difficult as the mineral resources currently buried in shallow, easy to exploit have gradually dried up. At present, exploration of traditional resources, such as copper, gold and cobalt, has become very difficult, with only 0. 5 per cent success in the exploration of new minerals and more than 99 per cent of conventional exploration projects having failed to become mines. Compared to traditional mine-seeking, ai technology is able to process and analyse a large amount of data quickly and accurately, significantly increasing the efficiency of exploration, narrowing the scope of exploration, collating mineral foresight in greenfield exploration projects where data are scarce, and discovering some mine-seeking information that has been ignored by traditional theories or methods。
Secondly, the human and time costs of mining development have been significantly reduced. In the course of mining exploration development, from initial access to extensive literature, collection of samples of wild rock, geological fillings, processing of data such as petrified remote drilling, mapping of mine target areas, establishment of a three-dimensional geological model of the mine, to mining, transport, ore selection, smelting, etc. At the production stage, to integrated production management, early warning of mine disasters, etc., the various elements require considerable time and effort on the part of people with expertise and skills to complete them, with significant labour and time costs. Ai has efficient computing and information processing capabilities that allow for automatic data extraction, integration and analytical modelling, as well as intelligent control of mining, mining and smelting equipment, significantly reducing labour and time costs and increasing productivity and resource utilization。
Thirdly, the management of mining production has been greatly optimized and security risks reduced. The limitations of human resources, equipment use and supplies in all aspects of mining production inevitably affect the efficiency of mine production and resource use. Through a comprehensive analysis of historical and real-time monitoring of mineral production data, ai technology facilitates the optimization of mining production chains and the efficient use of production management capacity and resources by accurately identifying production bottlenecks and their causes and achieving intelligent decision-making and control. At the same time, ai technology, through various sensors and monitoring devices in mines, allows for safe monitoring and risk warning of mines, detection of safety hazards and anomalies, early warning and appropriate security measures to reduce accidents。
05
There are still many bottlenecks in ai technology in the short term
One is the difficulty of obtaining and processing geological data. The earth has a long history of geological evolution, and mineralization is more a complex geological process, with geological exploration and exploitation data having the characteristics of anisomer, multi-scale, non-stereotyped and discontinuous. The largest applications of ai technology are premised on the availability and processing of large-scale data, such as deep geological information, large amounts of geological data, production data, equipment data, etc., involved in mining exploration and exploitation in a complex production environment, which still depend on traditional means。
Secondly, data quality and reliability of results need to be improved. Currently ai is highly dependent on high-quality large-data samples for smart applications in mining exploration and exploitation. Impacts such as heat disasters, rock explosions during the deep exploration process, environmental noise, disturbance during the development of mines, and the loss of data, errors, etc. Resulting from the artificial collection and collation of geological data can affect the quality of data and lead to a decrease in the accuracy and reliability of ai analysis. In addition, ai technology does not have common knowledge of human beings and may carry out decisions or actions that are contrary to social ethics, leading to uncontrolled results。
Thirdly, data privacy and security need to be strengthened. Currently, with a large number of ai model openings, hackers ' knowledge of artificial intelligence is becoming deeper. In the process of large-scale data analysis, as ai technologies rely heavily on big data, geological data relate to the protection of privacy and sensitive information, and the security of sensitive geological information and data against infringement becomes critical, balancing accessibility with prevention of misuse。
06
Our ai proposal for mining development
One is to build high-quality data platforms. The larger the geological data, the greater the practical application capacity of ai in mining exploration and exploitation, and the establishment of a high-quality data platform in the field of geological mining is a crucial step in the large-scale application of ai in the field of mining. The data involved in our new mine breakthrough strategy are numerous and require the integration and screening of different forms of data from different sensors (e. G. Satellites, drones, etc.), different data sources (e. G., exploration, exploration, remote sensing, drilling, testing and analysis), and high-quality, large data platforms。
Second is the continuous optimization of ai algorithms, algorithms and models. Algorithms, algorithms and modelling structures are core areas of ai. Our ai algorithms in the field of mining are largely based on international open source algorithms, with relatively weak autonomous algorithm models built using mine search models, and innovative ai studies of large models dedicated to mining and industry should be strengthened. In addition, computing depends mainly on the calculation speed that can be achieved by the chips, and the calculus of developing ai in the field of mining in our country depends on the level that the chips can achieve in general。
Third, there is a well-established data update maintenance mechanism. At the heart of ai is the statistical analysis of valid and real data, so the reliability of the data and the continuous updating of their optimization form the basis of ai's application in the mining industry. To address problems in data acquisition and processing, more efficient and accurate data collection and processing techniques are needed to enhance validation of data validity, establish a regular updating maintenance mechanism and improve the quality, reliability and consistency of basic data。
Fourth is strengthening data security management and protection. With the advent of the information age, the security and integrity of geological data is essential for the application of ai, and the safe management and protection of information must be given high priority. Appropriate data privacy protection mechanisms and security strategies need to be put in place to protect the confidentiality and integrity of critical data using strong password algorithms, encryption techniques, high security standards, etc., to ensure that only authorized persons are able to view and use relevant data and to prevent unauthorized access and improper use of sensitive data to meet changing privacy and security threats。
Fifth is to invest in cross-border integration innovations. Future trends in ai in the field of mining will also be reflected in cross-border integration and innovation. Technologies such as drones, robots, sensors, etc. Can address the challenges of collecting ai data; techniques such as cloud computing, big data can provide powerful computing support for ai applications in the field of terrestrial mines, accelerate the development of advanced algorithm models for geological exploration, and make the deployment and maintenance of ai systems easier and more efficient。
