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Technologies and Methods for Exploitation of Geological Resources, 2nd Edition
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Technologies and Methods for Exploitation of Geological Resources, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 20 November 2026 | Viewed by 3401

Special Issue Editors

Dr. Zhongliang Wang

E-Mail
Guest Editor
School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
Interests: structural geology; mineral exploration; mineral deposit
Special Issues, Collections and Topics in MDPI journals
Dr. Markos Tranos

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Guest Editor
Department of Geology, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: structural geology; geological mapping; neotectonics; basin analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hidden geological resources
  • exploration theory
  • technologies and methods
  • geological process
  • earth information
  • geochemical technology

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Published Papers (4 papers)

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Research

19 pages, 30013 KB  
Article
Karst Collapse Seepage Field Simulation and Prediction in Tuoshan Mine-Field of Jinzhushan Mining Area, Central Hunan, China
by Yingzi Chen, Ziqiang Zhu and Guangyin Lu
Appl. Sci. 2026, 16(8), 3998; https://doi.org/10.3390/app16083998 - 20 Apr 2026
Viewed by 325
Abstract
Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term [...] Read more.
Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term groundwater monitoring at six boreholes, and high-density electrical geophysics. A topographically corrected MODFLOW seepage-field model is developed and calibrated for 2014 (RMSE = 0.32 m; NSE = 0.85) and validated for 2015–2016 (RMSE = 0.41 m; NSE = 0.81). To address the large groundwater-level simulation errors commonly encountered in subtropical hilly karst mining settings, the model incorporates a topographic correction, improving simulation accuracy by 12% relative to an uncorrected model. The simulations capture rapid “steep rise–slow fall” groundwater dynamics: Heavy rainfall (>100 mm/day) raises groundwater levels by 2.8–3.1 m within 2–3 days, whereas pumping (200 m3/h) causes a 1.9–2.2 m decline within one week. A 1.2 km drawdown funnel forms and overlaps with 89% of collapse points, indicating that seepage-field evolution and groundwater-level decline control collapse clustering, with soil suffusion and soil–water–rock interaction acting as key amplifying processes. Based on Terzaghi’s effective stress principle and the Theis solution, a collapse prediction formula is derived and validated using measured events (accuracy = 87.5%), and a region-specific critical hydraulic gradient (in = 0.85) is determined, lower than values reported for North China. The proposed workflow provides quantitative thresholds and model-based guidance for karst collapse prevention in subtropical mining areas. Full article
(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources, 2nd Edition)
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22 pages, 10870 KB  
Article
Fracture Prediction Based on a Complex Lithology Fracture Facies Model: A Case Study from the Linxing Area, Ordos Basin
by Yangyang Zhao, Zhicheng Ren, Xiaoming Chen, Wenxiang He, Zhixuan Zhang, Zijian Wei and Yong Hu
Appl. Sci. 2025, 15(24), 13277; https://doi.org/10.3390/app152413277 - 18 Dec 2025
Viewed by 503
Abstract
In the Ordos Basin, the lengths of cores are disproportionate to image logging data (1:9) and fracture research is difficult because of their complex lithology and fracture patterns. Based on the characteristics of conventional logging and cores, this paper describes the color, shape, [...] Read more.
In the Ordos Basin, the lengths of cores are disproportionate to image logging data (1:9) and fracture research is difficult because of their complex lithology and fracture patterns. Based on the characteristics of conventional logging and cores, this paper describes the color, shape, geophysical characteristics and geological features of the basin to establish an image recognition template and to identify nine distinct lithologies. The genesis, type, occurrence, opening mode, cutting depth, host lithology, density and tectonic stress of the fractures are used to define four types of fracture facies (bedding fracture facies, N100° tectonic fracture facies, N10° tectonic fracture facies and coal fracture facies) and to build four models. The comprehensive coherence among the neural network results, curvatures, ant bodies, lithologies, and thicknesses was used to predict the type of different fracture facies. The results show that the fracture prediction model fully reflects the genesis of the cracks and influencing factors and provides insights into optimal areas for future exploration and development. Full article
(This article belongs to the Special Issue Technologies and Methods for Exploitation of Geological Resources, 2nd Edition)
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25 pages, 9002 KB  
Article
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
Cited by 1 | Viewed by 908
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 [...] Read more.
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  
Article
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
Viewed by 1115
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 [...] Read more.
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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