Mineral resources are of great significance in the development of the national economy. Prospecting and forecasting are the key to ensure the security of mineral resources supply, promote economic development, and maintain social stability. The methods for prospecting prediction have evolved from qualitative to quantitative prediction, from empirical research to mathematical analysis. In recent years, deep learning algorithms have gradually entered the attention of geologists due to their robust learning and simulation ability in the application of prospecting prediction. Deep learning algorithms can effectively analyze and predict data, which have great significance in improving the efficiency and accuracy of mineral exploration. However, there are not many specific examples of their application in mineral exploration prediction, and researchers have not yet conducted a comprehensive discussion on the advantages, disadvantages, and accuracy of deep learning algorithms in mineral prospectivity mapping applications. This paper reviews and discusses the application of deep learning in prospecting prediction, highlighting the challenges faced by deep learning in the application of prospecting prediction in data preprocessing, data enhancement, system parameter adjustment, and accuracy evaluation, and puts forward specific suggestions for research in these aspects. The purpose of this paper is to provide a reference for the application of deep learning to researchers and practitioners in the field of prospecting prediction.

Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management.

The Jinchuan area is located along the northeastern margin of Longshou Shan, in the western part of the North China Plate. Since the Paleoproterozoic period, it has undergone complex geological evolution. A systematic analysis of the tectonic evolution in this region reveals key information about the tectonic background and evolutionary characteristics since the Paleoproterozoic period and serves as a crucial approach for understanding metallogenic processes and achieving breakthroughs in deep mineral exploration. Based on detailed field investigations, this study analyzes the structural characteristics of the area, focusing on conjugate shear joints, folds, and faults. Combined with previous research findings, the evolution of the tectonic stress field is analyzed. The results indicate that the orientation of the maximum principal stress underwent the following six distinct phases of change: nearly north-south (NS) → nearly east-west (EW) → nearly north-south (NS) → north-northeast-south-southwest (NNE-SSW) → northwest-southeast (NW-SE) → northeast-southwest (NE-SW). Integrating these results with the regional tectonic framework, the study systematically reconstructs the tectonic evolution of the Jinchuan area. This research provides important scientific insights and practical value for enhancing geological understanding of the region and guiding mineral resource exploration and development.

There are many methods of forest resource survey, and so are the methods of stock estimation.The forest stock estimation method based on field survey data of sample plots is widely used in the industry because of its characteristics such as accurate data and simple operation. Based on the data obtained from 154 forest plots in the study area, the forest stock, biomass and carbon storage of typical county-level administrative units in the Hanjiang River Basin were estimated. The forest ecological function of the study area was evaluated.(1) The forest stock volume in the study area was 356.45×10⁴ m³, the stock volume per hectare was 71.43 m³, the number of plants per hectare was 853, and the average DBH was 13.3 cm.(2) The biomass of forest resources was 1193636.77 t, and the biomass density was 23.92 t/hm²; The vegetation carbon storage of forest ecosystem was 309966 t and the carbon density was 6.2 t/hm².(3) The forest resources in the study area were graded by grading the sample plots, and the ecological function level of the forest resources in the study area was grade 2.