The continuous growth in global demand for mineral resources and the increasing difficulty of mineral exploration have created bottlenecks for traditional mineral prediction methods in handling complex geological information and large amounts of data. This review aims to explore the latest research progress in machine learning technology in the field of large-scale mineral prediction from 2016 to 2025. By systematically searching the Web of Science core database, we have screened and analyzed 255 high-quality scientific studies. These studies cover key areas such as mineral information extraction, target area selection, mineral regularity modeling, and resource potential evaluation. The applied machine learning technologies include Random Forests, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, etc., and have been widely used in the exploration and prediction of various mineral deposits such as porphyry copper, sandstone uranium, and tin. The findings indicate a substantial shift within the discipline towards the utilization of deep learning methodologies and the integration of multi-source geological data. There is a notable rise in the deployment of cutting-edge techniques, including automatic feature extraction, transfer learning, and few-shot learning. This review endeavors to synthesize the prevailing state and prospective developmental trajectory of machine learning within the domain of large-scale mineral prediction. It seeks to delineate the field’s progression, spotlight pivotal research dilemmas, and pinpoint innovative breakthroughs.

Since the 1950s, numerous soil and water conservation measures have been implemented to control severe soil erosion in the Liuhe River Basin (LRB), China. While these measures have protected the upstream soil and water ecological environment, they have led to a sharp reduction in the downstream flow and the deterioration of the river ecological environment. Therefore, it is important to evaluate the impact of soil and water conservation measures on hydrological processes to assess long-term runoff changes. Using the Soil and Water Assessment Tool (SWAT) models and sensitivity analyses based on the Budyko hypothesis, this study quantitatively evaluated the effects of climate change, direct water withdrawal, and soil and water conservation measures on runoff in the LRB during different periods, including different responses to runoff discharge, hydrological regime, and flood processes. The runoff series were divided into a baseline period (1956–1969) and two altered periods, i.e., period 1 (1970–1999) and period 2 (2000–2020). Human activities were the main cause of the decrease in runoff during the altered periods, contributing 86.03% (−29.61 mm), while the contribution of climate change was only 13.70% (−4.70 mm). The impact of climate change manifests as a decrease in flood volume caused by a reduction in precipitation during the flood season. Analysis of two flood cases indicated a 66.00%–84.00% reduction in basin runoff capacity due to soil and water conservation measures in the upstream area. Soil and water conservation measures reduced the peak flow and total flood volume in the upstream runoff area by 77.98% and 55.16%, respectively, even with nearly double the precipitation. The runoff coefficient in the reservoir area without soil and water conservation measures was 4.0 times that in the conservation area. These results contribute to the re-evaluation of soil and water conservation hydrological effects and provide important guidance for water resource planning and water conservation policy formulation in the LRB.

With the rapid development of the global new-energy industry, lithium, as a key strategic mineral resource, has faced increasingly prominent supply–demand contradictions. The Anning area in Central Yunnan serves as an important clay-type lithium resource base in China, and its mineralization prediction research holds significant theoretical value and practical implications. This study focuses on the lithium-rich claystone of the Daoshitou Formation in the Anning area of Central Yunnan. Based on an in-depth analysis of typical clay-type lithium mineralization characteristics, this research analyzes regional lithium metallogenic conditions, identifies key ore-controlling factors and prospecting indicators, comprehensively collects geological, geophysical, geochemical, and remote sensing data, and completes digital processing. By integrating multi-source geoscience data, a multi-source geoscience information spatial database was constructed. Using the Random Forest algorithm as the core method and comparing with machine learning models such as XGBoost and Support Vector Machine (SVM), a mineralization prediction model was established to achieve quantitative prediction of clay-type lithium mineralization probability. The specific research findings are as follows: (1) The metallogenic model and prospecting indicators of clay-type lithium deposits were clarified. The Anning area of Central Yunnan mainly develops sedimentary clay-type lithium deposits, and mineralization is jointly controlled by lithium-rich source material supply, chemical weathering under warm and humid paleoclimate conditions, and weak reductive sedimentary environments of coastal-swamp facies; (2) a multi-source geoscience information database was constructed, extracting 16 predictive variables and establishing a metallogenic prediction sample dataset; (3) the Random Forest model performed optimally with an AUC value of 0.84, accuracy of 88.3%, precision of 89.4%, recall of 87.2%, and F1 score of 0.88; (4) three metallogenic prospect areas were delineated. The results indicate that under the study area conditions and existing data, the Random Forest model demonstrates good applicability and stability. The research results provide important target areas for lithium mineral exploration and deployment in the Anning area of Central Yunnan, and offer methodological references for similar deposit prediction research.

Mineral resource exploration is crucial for national economic security and sustainable development. Conventional mineral prediction methods struggle with low efficiency, high costs, and insufficient accuracy in predicting concealed deep - seated deposits and those in complex tectonic areas. Recently, machine learning algorithms, with powerful data - mining and pattern - recognition capabilities, have greatly enhanced mineral prediction intelligence. This paper comprehensively sums up the research progress and development trend of machine learning in large - scale mineral resource prediction, covering algorithm frameworks like random forests, support vector machines, and deep learning, as well as data - preprocessing techniques such as feature engineering and multi - source data fusion. For instance, the random forest algorithm achieved an 82% target - area match rate in Tibet's Julong porphyry Cu - Mo deposit prediction, cutting exploration costs by about 40%; in Fujian's southwestern Makeng iron deposit study, comparing four algorithms including random forests and support vector machines, the optimal model got an AUC of 0.88. However, issues like uneven data quality and poor model interpretability remain to be solved. In the future, combining generative adversarial networks (GANs) to boost data diversity and developing a "geological constraint + AI - driven" interdisciplinary paradigm are needed to promote the intelligent and precise transformation of mineral resource exploration.