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Dear Colleagues,

Building upon the success and valuable contributions of the first volume, we are pleased to announce the launch of the second edition of this Special Issue.

Geological resources, such as metals, coal, petroleum and gas, groundwater, and geothermal energy, are the essential basis for human survival. Most of the easy-to-find and easy-to-develop geological resources have been found, but the discovery of a huge amount of still-hidden geological resources is an issue of considerable concern. Therefore, the demand for revolutionary ideas and technologies for exploiting geological resources is urgent. The present Special Issue aims to gather papers on the geological resource exploration theory, method and technology research, including the reconstruction of the geological process of resource formation, geological and mineral geological survey methods, geochemical technology, earth information technology, earth exploration technology, and relevant engineering technology. Our goal is to offer exploitation paradigms for geological resources and provide a key basis to improve our knowledge about the nature and basic laws of deep geological resources, as well as promote the fundamental and frontier research for geological resource exploitation.

Dr. Zhongliang Wang
Dr. Markos Tranos
Guest Editors

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25 pages, 9002 KB

Open AccessArticle

Mapping Relationship Between Field and Laboratory Direct Shear Strength Indicators of Soil and Rock Layers at Shallow Depths in Arid–Hot Valley Regions

by Qinghe Zeng, Zhibin Li, Jin Liao, Hong Ke, Xionghui Huang, Xiangqing Li, Shoukui Wang, Zhen Liu and Cuiying Zhou

Appl. Sci. 2025, 15(22), 12241; https://doi.org/10.3390/app152212241 - 18 Nov 2025

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Abstract

The arid–hot valley regions in southwestern China are characterized by developed geological structures and frequent local heavy rainfalls, which often trigger flash floods. The mechanical properties of soil and rock masses in these regions are critical for the construction of regional projects. Field direct shear tests can accurately reflect the mechanical properties of the soil and rock masses in their natural state, but they are costly and cause significant disturbance to the surrounding environment. In contrast, laboratory direct shear tests are more straightforward and cost-effective but cannot fully replicate the complex stress conditions and structural characteristics of in situ soil and rock masses. The lack of correlation between field and laboratory direct shear strength indicators significantly hinders the accurate assessment of geotechnical properties, thereby affecting the precision of engineering applications. To this end, this paper focuses on the soil and rock layers in the arid–hot valley regions in southwestern China. This research took into account the effects of soil depth and moisture content, proposing a solution that fully correlates field and laboratory direct shear strength test indicators. Field and laboratory direct shear tests were conducted at shallow depths to investigate the relationship between the shear strength indicators of various geological formations. The results show that laboratory remolded sample tests generally yield lower shear strength values compared to field direct shear tests. The laboratory shear strength and internal friction angle of each rock and soil layer show a linear increase with depth. A mathematical relationship between soil layer depth, laboratory shear strength indicators, and field shear strength indicators can be established using a quadratic polynomial function. This resolved the “disconnect” between field and laboratory test results, significantly reducing engineering survey costs and providing important theoretical basis and reference for engineering construction in arid and hot river valley regions. Full article

(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources, 2nd Edition)

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23 pages, 5632 KB

Open AccessArticle

Classification of Rockburst Intensity Grades: A Method Integrating k-Medoids-SMOTE and BSLO-RF

by Qinzheng Wu, Bing Dai, Danli Li, Hanwen Jia and Penggang Li

Appl. Sci. 2025, 15(16), 9045; https://doi.org/10.3390/app15169045 - 16 Aug 2025

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Abstract

Precise forecasting of rockburst intensity categories is vital to safeguarding operational safety and refining design protocols in deep underground engineering. This study proposes an intelligent forecasting framework through the integration of k-medoids-SMOTE and the BSLO-optimized Random Forest (BSLO-RF) algorithm. A curated dataset encompassing 351 rockburst instances, stratified into four intensity grades, was compiled via systematic literature synthesis. To mitigate data imbalance and outlier interference, z-score normalization and k-medoids-SMOTE oversampling were implemented, with t-SNE visualization confirming improved inter-class distinguishability. Notably, the BSLO algorithm was utilized for hyperparameter tuning of the Random Forest model, thereby strengthening its global search and local refinement capabilities. Comparative analyses revealed that the optimized BSLO-RF framework outperformed conventional machine learning methods (e.g., BSLO-SVM, BSLO-BP), achieving an average prediction accuracy of 89.16% on the balanced dataset—accompanied by a recall of 87.5% and F1-score of 0.88. It exhibited superior performance in predicting extreme grades: 93.3% accuracy for Level I (no rockburst) and 87.9% for Level IV (severe rockburst), exceeding BSLO-SVM (75.8% for Level IV) and BSLO-BP (72.7% for Level IV). Field validation via the Zhongnanshan Tunnel project further corroborated its reliability, yielding an 80% prediction accuracy (four out of five cases correctly classified) and verifying its adaptability to complex geological settings. This research introduces a robust intelligent classification approach for rockburst intensity, offering actionable insights for risk assessment and mitigation in deep mining and tunneling initiatives. Full article

(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources, 2nd Edition)

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