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Mining Deformation Disasters and Eco-Geological Environment Monitoring
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Mining Deformation Disasters and Eco-Geological Environment Monitoring

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 8319

Special Issue Editor

Dr. Peixian Li

E-Mail Website
Guest Editor
College of Geosciences and Surveying Engineering, China University of Mining and Technology Beijing 100083, China
Interests: mining subsidence and rock movement

Special Issue Information

Dear Colleagues,

Over the years, high-intensity resource development has made great contributions to the rapid development of the economies and societies of the world, but it has also given rise to a vast arena of mine geological disasters, such as surface subsidence, ground fissures, collapses, landslides, and debris flow, resulting in the destruction of landforms and landscapes, land resources, and water resources, threatening the lives and property safety of residents in the mining area and restricting the sustainable development of regional economy and society. In recent years, a number of new technologies and methods, such as InSAR monitoring, high-precision remote sensing, and UAV aerial photography, have been applied in mine geological disaster monitoring, prevention, and early warning, and remarkable results have been achieved.

In this Special Issue, original research articles and reviews of mining deformation disasters and eco-geological environment monitoring are welcome. Contributions can be from different research backgrounds, including mining subsidence, rock movement and its control, deformation monitoring, mining environment changes, InSAR and GNSS data processing etc. We envisage that this Special Issue will be concluded in 2023. Specific research areas of interest include (but are not limited to) the following:

(1) Reviews and prospects of the application of new surveying and mapping science and technology in the field of mine geological disaster prevention, control, and early warning in the new era.

(2) Studies in mining areas based on InSAR, UAV, lidar, GNSS, hyperspectral, near infrared, thermal infrared remote sensing and other technologies in addition to the intelligent identification, monitoring, and disaster causing law of geological disasters (surface subsidence, ground fissures, coal fires, landslides, collapses, debris flows, etc.).

(3) Efficient and intelligent processing algorithms for multi-source monitoring data.

(4) Intelligent technology for early warning of geological hazards in mining areas.

I look forward to receiving your contributions.

 

Dr. Peixian Li
Guest Editor

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Keywords

  • Keywords: mining subsidence
  • deformation monitoring
  • mining environment protection
  • geological hazards
  • eco-geological environment
  • multi-source data fusion
  • green mining
  • smart mine
  • Internet of Things
  • disaster forecast
  • hazards and risk assessment

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Published Papers (5 papers)

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18 pages, 10495 KiB  
Article
Improving Differential Interferometry Synthetic Aperture Radar Phase Unwrapping Accuracy with Global Navigation Satellite System Monitoring Data
by Hui Wang, Yuxi Cao, Guorui Wang, Peixian Li, Jia Zhang and Yongfeng Gong
Sustainability 2023, 15(17), 13277; https://doi.org/10.3390/su151713277 - 4 Sep 2023
Cited by 1 | Viewed by 1111
Abstract
: We developed a GNSS-assisted InSAR phase unwrapping algorithm for large-deformation DInSAR data processing in coal mining areas. Utilizing the Markov random field (MRF) theory and simulated annealing, the algorithm derived the energy function using MRF theory, Gibbs distribution, and the Hammersley–Clifford theorem. [...] Read more.
: We developed a GNSS-assisted InSAR phase unwrapping algorithm for large-deformation DInSAR data processing in coal mining areas. Utilizing the Markov random field (MRF) theory and simulated annealing, the algorithm derived the energy function using MRF theory, Gibbs distribution, and the Hammersley–Clifford theorem. It calculated an image probability ratio and unwrapped the phase through iterative calculations of the initial integer perimeter matrix, interference phase, and weight matrix. Algorithm reliability was confirmed by combining simulated phases with digital elevation model (DEM) data for deconvolution calculations, showing good agreement with real phase-value results (median error: 4.8 × 10−4). Applied to ALOS-2 data in the Jinfeng mining area, the algorithm transformed interferometric phase into deformation, obtaining simulated deformation by fitting GNSS monitoring data. It effectively solved meter-scale deformation variables between single-period images, particularly for unwrapping problems due to decoherence. To improve calculation speed, a coherence-based threshold was set. Points with high coherence avoided iterative optimization, while points below the threshold underwent iterative optimization (coherence threshold: 0.32). The algorithm achieved a median error of 30.29 mm and a relative error of 2.5% compared to GNSS fitting results, meeting accuracy requirements for mining subsidence monitoring in large mining areas. Full article
(This article belongs to the Special Issue Mining Deformation Disasters and Eco-Geological Environment Monitoring)
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16 pages, 2301 KiB  
Article
Detection of Abnormal Data in GNSS Coordinate Series Based on an Improved Cumulative Sum
by Chao Liu, Qingjie Xu, Ya Fan, Hao Wu, Jian Chen and Peng Lin
Sustainability 2023, 15(9), 7228; https://doi.org/10.3390/su15097228 - 26 Apr 2023
Cited by 1 | Viewed by 1054
Abstract
The global navigation satellite system (GNSS), as a high-time resolution and high-precision measurement technology, has been widely used in the field of deformation monitoring. Owing to the influence of uncontrollable factors, there are inevitably some abnormal data in the GNSS monitoring series. Thus, [...] Read more.
The global navigation satellite system (GNSS), as a high-time resolution and high-precision measurement technology, has been widely used in the field of deformation monitoring. Owing to the influence of uncontrollable factors, there are inevitably some abnormal data in the GNSS monitoring series. Thus, it is necessary to detect and identify abnormal data in the GNSS monitoring series to improve the accuracy and reliability of the deformation disaster law analysis and warning. Many methods can be used to detect abnormal data, among which the statistical process control theory, represented by the cumulative sum (CUSUM), is widely used. CUSUM usually constructs statistics and determines control limits based on the threshold criteria of the average run length (ARL) and then uses the control limits to identify abnormal data in CUSUM statistics. However, different degrees of the ‘trailing’ phenomenon exist in the interval of abnormal data identified by the algorithm, leading to a higher false alarm rate. Therefore, we propose an improved CUSUM method that uses breaks for additive season and trend (BFAST) instead of ARL-based control limits to identify abnormal data in CUSUM statistics to improve the accuracy of identification. The improved CUSUM method is used to detect abnormal data in the GNSS coordinate series. The results show that compared with CUSUM, the improved CUSUM method shows stronger robustness, more accurate detection of abnormal data, and a significantly lower false alarm rate. Full article
(This article belongs to the Special Issue Mining Deformation Disasters and Eco-Geological Environment Monitoring)
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16 pages, 15607 KiB  
Article
Deformation Information Extraction from Multi-GNSS Coordinate Series Based on EWT-ICA-R
by Runfa Tong, Chao Liu, Yuan Tao, Ya Fan and Jian Chen
Sustainability 2023, 15(5), 4578; https://doi.org/10.3390/su15054578 - 3 Mar 2023
Cited by 2 | Viewed by 1542
Abstract
Global navigation satellite system (GNSS) has been widely used in many deformation monitoring fields in recent years and can achieve centimeter-level or even sub-centimeter-level real-time monitoring accuracy through the carrier phase double-differenced technique. However, this technique cannot eliminate or weaken multipath errors, which [...] Read more.
Global navigation satellite system (GNSS) has been widely used in many deformation monitoring fields in recent years and can achieve centimeter-level or even sub-centimeter-level real-time monitoring accuracy through the carrier phase double-differenced technique. However, this technique cannot eliminate or weaken multipath errors, which become the main error source for GNSS deformation monitoring. Therefore, extracting deformation information from coordinate series mixed with multipath errors has become a key issue for further improving the accuracy of GNSS deformation monitoring. In this paper, we propose an approach to overcome this issue called empirical wavelet transform-independent component analysis with reference (EWT-ICA-R). The specific process is as follows. First, EWT is employed to model the multipath errors from a priori GNSS coordinate series, and the model is input to ICA-R as a reference signal. Then, the GNSS deformation monitoring series mixed with multipath errors and deformation information is decomposed into sub-series of different scales using EWT, and these sub-series are input to ICA-R as multi-channel signals. Finally, ICA-R is used to calculate the input signals together to obtain the multipath errors in the GNSS deformation monitoring series and then subtract the multipath errors from the GNSS deformation monitoring series to obtain accurate deformation information. Experiments show the following: (1) For the vibration deformation experiments, the correlation coefficients between the deformation information extracted by the proposed method and the real values reached 0.981, 0.981, and 0.885 in the E, N, and U directions, respectively, and the corresponding root mean square errors decrease to 0.694 mm, 0.694 mm, 1.852 mm, respectively. (2) For the slow-deformation experiment, the correlation coefficients in the three directions were all higher than 0.98, and the corresponding root mean square errors decrease to 1.345 mm, 1.546 mm, and 3.866 mm, respectively. The experiments verified the feasibility of the proposed method to accurately extract deformation information, which makes it possible to obtain sub-millimeter GNSS deformation information and provide effective technical support for deformation monitoring in related fields. Full article
(This article belongs to the Special Issue Mining Deformation Disasters and Eco-Geological Environment Monitoring)
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15 pages, 14542 KiB  
Article
A Novel Deformation Extraction Approach for Sub-Band InSAR and Its Application in Large-Scale Surface Mining Subsidence Monitoring
by Xinpeng Diao, Quanshuai Sun, Jing Yang, Kan Wu and Xin Lu
Sustainability 2023, 15(1), 354; https://doi.org/10.3390/su15010354 - 26 Dec 2022
Cited by 3 | Viewed by 1898
Abstract
Differential synthetic aperture radar interferometry (InSAR) is widely used to monitor ground surface deformation due to its wide coverage and high accuracy. However, the large-scale and rapid deformation that occurs in mining areas often leads to densely spaced interference fringes, thus, severely limiting [...] Read more.
Differential synthetic aperture radar interferometry (InSAR) is widely used to monitor ground surface deformation due to its wide coverage and high accuracy. However, the large-scale and rapid deformation that occurs in mining areas often leads to densely spaced interference fringes, thus, severely limiting the applicability of D-InSAR in mining subsidence monitoring. Sub-band InSAR can reduce phase gradients in interferograms by increasing the simulated wavelength, thereby characterising large-scale surface deformations. Nonetheless, accurate registration between non-overlapping sub-band images with conventional sub-band InSAR is challenging. Therefore, our study proposed a new sub-band InSAR deformation extraction method, based on raw full-bandwidth single-look complex image pair registration data to facilitate sub-band interferometric processing. Simulations under noiseless conditions demonstrated that the maximum difference between the sub-band InSAR-monitored results and real surface deformations was 26 mm (1.86% of maximum vertical deformation), which theoretically meets the requirements for mining subsidence monitoring. However, when modelling dynamic deformation with noise, the sub-band InSAR-simulated wavelength could not be optimised for surface deformation due to the limitation in current SAR satellite bandwidths, which resulted in significantly noisy and undistinguishable interference fringes. Nonetheless, this method could still be advantageous in high-coherence regions where surface deformation exceeds 1/5th of the simulated wavelength. Full article
(This article belongs to the Special Issue Mining Deformation Disasters and Eco-Geological Environment Monitoring)
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12 pages, 2927 KiB  
Study Protocol
Automatic-Detection Method for Mining Subsidence Basins Based on InSAR and CNN-AFSA-SVM
by Lei Wang, Shibao Li, Chaoqun Teng, Chuang Jiang, Jingyu Li, Zhong Li and Jinzhong Huang
Sustainability 2022, 14(21), 13898; https://doi.org/10.3390/su142113898 - 26 Oct 2022
Cited by 4 | Viewed by 1452
Abstract
Mining subsidence disasters are common geological disasters. Accurate and effective identification of their deformation position is significant in preventing and controlling geological disasters and monitoring illegal mining. In this study, deep learning, combined with a support vector machine (SVM), has been used to [...] Read more.
Mining subsidence disasters are common geological disasters. Accurate and effective identification of their deformation position is significant in preventing and controlling geological disasters and monitoring illegal mining. In this study, deep learning, combined with a support vector machine (SVM), has been used to establish an automatic-detection method for mining subsidence basins using Sentinel-1A data. The Huainan mining area was selected as the experimental area to verify the method. The interferogram was obtained using differential radar interferometry (D-InSAR) to process the Sentinel-1A radar data of seven landscapes, and the mining subsidence basin and other targets were extracted manually as training samples. Subsequently, AlexNet, VGG19, and ResNet50 convolutional neural networks (CNNs) were used to extract feature vectors of mining subsidence basins for the SVM classifier, and mining subsidence basins were detected in a large-area InSAR interferogram. Non-maximum suppression was used to remove the repeated search box to improve the detection accuracy of mining subsidence basins; the artificial fish swarm algorithm with strong optimization ability and good global convergence is introduced into SVM parameter optimization to construct an improved ResNet50_SVM model. The experimental results show that: (1) the three CNN_SVM methods can accurately detect dry-mining subsidence basins automatically in large regional interference maps, providing an essential scientific basis for the government to monitor illegal mining activities and prevent and control geological disasters in mining areas; (2) the accuracy of the CNN_SVM automatic-detection methods for mining subsidence basins is approximately 80%, and that of ResNet50_SVM for mining subsidence basin detection is 83.7%, superior to that of AlexNet_SVM and VGG19_SVM; (3) the accuracy of the improved ResNet50_SVM based on AFSA algorithm is 88.3%, which is better than the unimproved Resnet50_SVM model. Full article
(This article belongs to the Special Issue Mining Deformation Disasters and Eco-Geological Environment Monitoring)
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