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Minerals
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Modeling and Analysis of Mining Area using Remote Sensing and...
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Modeling and Analysis of Mining Area using Remote Sensing and GIS Technology

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 7963

Special Issue Editor

Dr. Jan Blachowski

E-Mail Website
Guest Editor
Department of Geodesy and Geoinformation, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland
Interests: modeling and analysis of natural and anthropogenic systems in geographic information systems (GISs); spatial statistics; spatial information infrastructure; deformation of mining and post-mining areas; mining surveying
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mining and post-mining areas are prone to the occurrence of various phenomena that pose a threat to the health of the environment and to human safety. These include: continuous and discontinuous ground deformation, induced seismicity, deterioration of soil and vegetation condition, change of water conditions, pollution, as well as other processes associated with the underground and open-cast mining operations or extraction of hydrocarbons and groundwater. Many aspects of these processes and their effects are still not fully recognized. Closure of mines is followed by the renaturation of post-mining areas. This is a continuous task that requires high resolution and often wide-area monitoring for reliable management of renaturation processes.

Proliferation of multi-resolution radar and spectral sensors and advances in spatial data analytics, especially data-driven methods in recent decades, allow for comprehensive and reliable monitoring, analysis and modelling of ground deformation, vegetation condition, acidic mine drainage (AMD), as well as other processes occurring on mining and post-mining grounds. Time-series analysis facilitates robust modelling, prediction, and effective learning of these anthropogenic processes.

Therefore, in in this Special Issue, the relevant original research articles, reviews, and technical notes are welcome. Topics include, but are not limited to, the following:

- applied earth observations for mining activity monitoring;

- assessment of mining activities to mitigate their environmental impacts;

- monitoring of mining infrastructure to improve efficiency and safety of operations;

- survey of post mining landscape rehabilitation;

- multi-source data fusion for analysis and modelling of mining areas;

- application of GIS, machine learning and deep learning for modelling of spatial relationships in mining areas;

- case studies of innovative methods and applications.

Dr. Jan Blachowski
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Minerals is an international peer-reviewed open access monthly 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

  • mine area monitoring
  • post-mining rehabilitation
  • remote sensing
  • data fusion
  • geospatial analysis
  • spatial data mining

Published Papers (4 papers)

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Research

16 pages, 21322 KiB  
Article
Mining Ground Deformation Estimation Based on Pre-Processed InSAR Open Data—A Norwegian Case Study
by Jan Blachowski and Steinar L. Ellefmo
Minerals 2023, 13(3), 328; https://doi.org/10.3390/min13030328 - 26 Feb 2023
Cited by 2 | Viewed by 1733
Abstract
Sublevel caving mining causes surface deformation in three distinct zones parallel to the extracted deposit. Most of the published research is focused on the extent of the caved and fracture zones. The extent of the largest, continuous deformation zone and, thus, the influence [...] Read more.
Sublevel caving mining causes surface deformation in three distinct zones parallel to the extracted deposit. Most of the published research is focused on the extent of the caved and fracture zones. The extent of the largest, continuous deformation zone and, thus, the influence of the mine on its surroundings is not yet fully documented. This study aimed at assessing the extent of surface deformation caused by the mining of a steep iron ore deposit in Norway. For this purpose, an innovative combination of the permanent scatterer (PS) InSAR technique and line-of-sight (LOS) movement data provided by a public web service and geographic information system (GIS) spatial interpolation methods was proposed. Two ascending tracks’ (A102 and A175) datasets spanning the period of 3 June 2016–11 October 2021 were used. Three interpolation methods, inverse distance weighted (IDW), radial basis function (RBF) and ordinary kriging (OK), were analysed in terms of their performance for mapping continuous deformation. The RBF and OK methods with anisotropy returned the lowest root mean square error (RMSE) values. The obtained difference in the maximum extent of deformation amounted to 26 m for the track A102 dataset and 44.5 m for the track A175 dataset, depending on the interpolation method used. The estimated maximum extent of the continuous deformation zone on the hanging-wall side of the sublevel caving mining operation is 663 m. This corresponds to a limit angle of 38.7 degrees, which is lower than in previously published studies. The results show that the influence of sublevel caving mining on the surroundings can be greater than previously thought. The usefulness of public PSInSAR data available from a national online service and spatial interpolation methods for determining the area of mining terrain deformations has been proven. The proposed approach provides a low-cost alternative and complementation for surveys performed about the mine and it is argued that it should be implemented as part of the mine’s monitoring system. Full article
(This article belongs to the Special Issue Modeling and Analysis of Mining Area using Remote Sensing and GIS Technology)
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19 pages, 6618 KiB  
Article
Integration of Remote Sensing and Field Data in Ophiolite Investigations: A Case Study of Logar Ophiolite Complex, SE Afghanistan
by Atal Yousufi, Hemayatullah Ahmadi, Alma Bekbotayeva, Yalkunzhan Arshamov, Akmaral Baisalova, Gulnara Omarova and Emrah Pekkan
Minerals 2023, 13(2), 234; https://doi.org/10.3390/min13020234 - 07 Feb 2023
Cited by 3 | Viewed by 2300
Abstract
Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. [...] Read more.
Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. The spatial distribution of the ophiolitic complex was examined in this study using the mineralogical indices (MI), band ratio (BR), and spectral angle mapper (SAM) methods within the framework of geospatial technology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Additionally, several samples were collected from the identified complexes for validation, petrographic, and mineralogical analyses. Combining geospatial technology and conventional approaches, e.g., field sampling and geological data analysis yields efficient discrimination of mafic–ultramafic rocks with their associated hydrothermal altered minerals. The serpentinization and carbonate processes are predominantly seen along the eastern side of the active fault zone following the detection of ophiolites. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined mafic index (MI), band ratio 12/14 and 4/8 for rocks and SAM for highlighting the mafic–ultramafic altered minerals, and petrographic analysis of the collected samples. The field works verified the results of the ASTER data. The findings of this study can significantly contribute to detailed tectonic and geologic studies of the detected ophiolites in terms of their emplacement mechanism and ages. Full article
(This article belongs to the Special Issue Modeling and Analysis of Mining Area using Remote Sensing and GIS Technology)
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15 pages, 5367 KiB  
Article
Machine Learning Model of Hydrothermal Vein Copper Deposits at Meso-Low Temperatures Based on Visible-Near Infrared Parallel Polarized Reflectance Spectroscopy
by Banglong Pan, Hanming Yu, Hongwei Cheng, Shuhua Du, Shaoru Feng, Ying Shu, Juan Du and Huaming Xie
Minerals 2022, 12(11), 1451; https://doi.org/10.3390/min12111451 - 17 Nov 2022
Cited by 2 | Viewed by 1543
Abstract
The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but [...] Read more.
The verification efficiency and precision of copper ore grade has a great influence on copper ore mining. At present, the common method for the exploration of reserves often uses chemical analysis and identification, which have high costs, long cycles, and pollution risks but cannot realize the in situ determination of the copper grade. The existing scalar spectrometric techniques generally have limited accuracy. As a vector spectrum, polarization state information is sensitive to mineral particle distribution and composition, which is conducive to high-precision detection. Taking the visible-near infrared parallel polarization reflectance spectrum data and grade data of a copper mine in Xiaoyuan village, Huaining County, Anhui Province, China, as an example, the characteristics of the parallel polarization spectra of the copper mine were analyzed. The spectra were pretreated by first-order derivative transform and wavelet denoising, and the dimensions of wavelet denoising spectra, parallel polarization spectra, and first-order derivative spectra were also reduced by principal component analysis (PCA). Three, four, and eight principal components of the three types of spectra were selected as variables. Four machine learning models, the radial basis function (RBF), support vector machine (SVM), generalized regression neural network (GRNN), and partial least squares regression (PLSR), were selected to establish the PCA parallel polarization reflectance spectrum and copper grade prediction model. The accuracy of the model was evaluated by the determination coefficient (R2) and root mean square error (RMSE). The results show that, for parallel polarization spectra, first-order derivative spectra, and wavelet denoising spectra, the PCA-SVM model has better results, with R2 values of 0.911, 0.942, and 0.953 and RMSE values of 0.022, 0.019, and 0.017, respectively. This method can effectively reduce the redundancy of polarized hyperspectral data, has better model prediction ability, and provides a useful exploration for the grade analysis of hydrothermal copper deposits at meso-low temperatures. Full article
(This article belongs to the Special Issue Modeling and Analysis of Mining Area using Remote Sensing and GIS Technology)
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17 pages, 3001 KiB  
Article
Comparative Analysis of Theoretical, Observational, and Modeled Deformation of Ground Subsidence: The Case of the Alhada Pb-Zn Mine
by Liming He, Jiuyang Cai, Wang Cao, Yachun Mao, Honglei Liu, Kai Guan, Yabo Zhou, Yumeng Wang, Jiashuai Kang, Xingjie Wang and Panke Pei
Minerals 2022, 12(8), 977; https://doi.org/10.3390/min12080977 - 31 Jul 2022
Cited by 1 | Viewed by 1314
Abstract
In this study, the probability integral method, Synthetic Aperture Radar Interferometry (InSAR), and the Okada dislocation model were collaboratively used to analyze deformation in the Alhada Pb-Zn mine. The predicted deformation values of the subsidence centers in three subsidence areas were 107 mm, [...] Read more.
In this study, the probability integral method, Synthetic Aperture Radar Interferometry (InSAR), and the Okada dislocation model were collaboratively used to analyze deformation in the Alhada Pb-Zn mine. The predicted deformation values of the subsidence centers in three subsidence areas were 107 mm, 120 mm, and 83 mm, respectively, as predicted using the probability integral method. The coherent scatterer InSAR technique was used to analyze the time-series deformation of the mining area, and the same subsidence center locations and similar deformation values were observed. The Okada dislocation model was used to invert the optimal parameters of the underground-mining ore body causing the surface subsidence, indicating that the surface subsidence is mainly caused by the mining of ore bodies in the 888 and 848 middle sections. We further simulated ground deformation using the multi-source Okada model. The results showed that the predicted and modeled deformations are highly correlated with the observed deformation. Through the analysis and comparison of the InSAR results, it was concluded that the three subsidence areas do not threaten the stability of the main buildings in the mining area. Using theoretical, observational, and modeling methods, the development and evolution of the subsidence area in mines can be established, which could provide basic data for subsidence control work and guarantee mine production safety. Full article
(This article belongs to the Special Issue Modeling and Analysis of Mining Area using Remote Sensing and GIS Technology)
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