## 1 Introduction  
Iron ore is one of the indispensable resources in the world, playing a key role in the development of various sectors such as metallurgy, chemicals, construction, transportation, and machinery. China is a major energy-consuming country, and although it possesses abundant iron ore reserves, the country's resources cannot fully meet domestic demand due to a low ore grade and mining technology limitations. As a result, most of the high-grade iron ore resources are still reliant on imports, with China accounting for more than 60% of global iron ore imports [1]. The Yanshan Iron Ore Mine is part of the giant Sijia Ying ore deposit and, like other iron ore deposits in the eastern Hebei region, is large in scale, but characterized by low-grade ones and long mining histories. These deposits are facing the issue of exhausted proven mineral reserves, making it urgent to explore deeper ore potentials [2,3]. Moreover, the Yanshan ore deposit is complex in terms of ore body distribution, with many intercalated rocks and unclear boundaries between ore bodies and surrounding rocks, leading to the challenge of low iron ore recovery. Currently, the main task of mineral resource quantitative prediction is to establish metallogenic models based on the comprehensive analysis of multi-source geoscientific information, including geology, geophysics, geochemistry, and remote sensing. This process involves constructing mathematical models, extracting and identifying favorable mineralization information, and conducting data fusion to create mineral prediction models. These models are used for the quantitative evaluation of potential mineral resources and to delineate exploration targets [3]. Integrating geoscience data from different dimensions, types, and scales for comprehensive analysis and modeling also helps geologists effectively identify geological backgrounds, metallogenic characteristics, and exploration directions. With the continuous development of real-time monitoring technologies such as \"Sky-Earth-Deep\" multi-scale multi-scenario observations and 5G+ communication, geoscience big data have undergone significant advancements and innovations, providing opportunities for the development of machine learning in the field of Earth sciences. This is particularly true in areas such as mineral exploration [4, 5, 6], remote sensing image analysis [7, 8, 9], and geological disaster prediction and risk assessment [10, 11, 12]. Nowadays, with the widespread acceptance of the concept of green sustainable development, the construction of intelligent mines under the integrated management of \"resources-environment-economy\" has become mainstream. In addition, considering the existing issues in deposits like the Yanshan Iron Ore Mine, the use of multi-source geoscience big data combined with methods like machine learning for quantitative prediction of mineral resources in mining areas has become crucial. Machine learning has been widely applied in various fields of mineral quantitative prediction research, achieving remarkable results, including in remote sensing alteration interpretation [13, 14, 15], three-dimensional/four-dimensional mineralization prediction [16; 17; 18; 19; 20], element grade analysis [21; 22], and environmental monitoring of mining areas [23]. Machine learning is divided into two categories: data-driven and knowledge-driven. Data-driven approaches include supervised, unsupervised, and semi-supervised learning, which use the locations of known mineral occurrences as labeled data to construct relationships between feature variables and predictive variables and employ mathematical methods for integrated decision-making [24]. Knowledge-driven approaches, on the other hand, are based on expert knowledge to assign weights to feature variables for decision-making, being suitable for targets with no or few known occurrences [25].