Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques
The occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/6/1046 |
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| author | Tao Tao Keming Han Xin Yao Ximing Chen Zuoqi Wu Chuangchuang Yao Xuwen Tian Zhenkai Zhou Kaiyu Ren |
| author_facet | Tao Tao Keming Han Xin Yao Ximing Chen Zuoqi Wu Chuangchuang Yao Xuwen Tian Zhenkai Zhou Kaiyu Ren |
| author_sort | Tao Tao |
| collection | DOAJ |
| description | The occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters, posing a serious threat to people’s lives, property, and mining production. Therefore, it is particularly important to quickly and accurately obtain the information of ground fissures and then study their distribution patterns and the law of spatial-temporal evolution. The traditional field investigation methods for identifying fissures have low efficiency. The rapid development of UAVs has brought an opportunity to address this issue. However, it also poses new questions, such as how to interpret numerous fissures and the distribution law of fissures with underground mining. Taking a mine in the Shenfu coalfield on the semi-desert aeolian sand surface as the research area, this paper studies the fissure recognition from UAV images by deep learning, fissure development law, as well as the mutual feed of surface condition corresponding to the under-ground mining progress. The results show that the DRs-UNet deep learning method can identify more than 85% of the fissures; however, due to the influence of seasonal vegetation changes and different fissure development stages, the continuity and integrity of fissure recognition methods need to be improved. Four fissure distribution patterns were found. In open-cut areas, arc-shaped fissures are frequently observed, displaying significant dimensions in terms of depth, length, and width. Within subsidence basins, central collapse areas exhibit fissures that form perpendicular to the direction of the working face. Along roadways, parallel or oblique fissures tend to develop at specific angles. In regions characterized by weak roof strata and depressed basins, abnormal reverse-“C”-shaped fissures emerge along the mining direction. The research results comprehensively demonstrate the process of automatically identifying ground fissures from UAV images as well as the spatial distribution patterns of fissures, which can provide technical support for the prediction of ground fissures, monitoring of geological hazards in mining areas, control of land environmental damage, and land ecological restoration. In the future, it is suggested that this method be applied to different mining areas and geotechnical contexts to enhance its applicability and effectiveness. |
| first_indexed | 2024-04-24T17:52:00Z |
| format | Article |
| id | doaj.art-aeaa8dd60c574e6abc6f36b0b6469c0b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T17:52:00Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-aeaa8dd60c574e6abc6f36b0b6469c0b2024-03-27T14:02:42ZengMDPI AGRemote Sensing2072-42922024-03-01166104610.3390/rs16061046Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning TechniquesTao Tao0Keming Han1Xin Yao2Ximing Chen3Zuoqi Wu4Chuangchuang Yao5Xuwen Tian6Zhenkai Zhou7Kaiyu Ren8Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaCCTEG Ecological Environment Technology Co., Ltd., Beijing 100013, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaCCTEG Ecological Environment Technology Co., Ltd., Beijing 100013, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaThe occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters, posing a serious threat to people’s lives, property, and mining production. Therefore, it is particularly important to quickly and accurately obtain the information of ground fissures and then study their distribution patterns and the law of spatial-temporal evolution. The traditional field investigation methods for identifying fissures have low efficiency. The rapid development of UAVs has brought an opportunity to address this issue. However, it also poses new questions, such as how to interpret numerous fissures and the distribution law of fissures with underground mining. Taking a mine in the Shenfu coalfield on the semi-desert aeolian sand surface as the research area, this paper studies the fissure recognition from UAV images by deep learning, fissure development law, as well as the mutual feed of surface condition corresponding to the under-ground mining progress. The results show that the DRs-UNet deep learning method can identify more than 85% of the fissures; however, due to the influence of seasonal vegetation changes and different fissure development stages, the continuity and integrity of fissure recognition methods need to be improved. Four fissure distribution patterns were found. In open-cut areas, arc-shaped fissures are frequently observed, displaying significant dimensions in terms of depth, length, and width. Within subsidence basins, central collapse areas exhibit fissures that form perpendicular to the direction of the working face. Along roadways, parallel or oblique fissures tend to develop at specific angles. In regions characterized by weak roof strata and depressed basins, abnormal reverse-“C”-shaped fissures emerge along the mining direction. The research results comprehensively demonstrate the process of automatically identifying ground fissures from UAV images as well as the spatial distribution patterns of fissures, which can provide technical support for the prediction of ground fissures, monitoring of geological hazards in mining areas, control of land environmental damage, and land ecological restoration. In the future, it is suggested that this method be applied to different mining areas and geotechnical contexts to enhance its applicability and effectiveness.https://www.mdpi.com/2072-4292/16/6/1046mining strata movement lawmining ground fissureUAVsautomatic identificationShenfu coalfield |
| spellingShingle | Tao Tao Keming Han Xin Yao Ximing Chen Zuoqi Wu Chuangchuang Yao Xuwen Tian Zhenkai Zhou Kaiyu Ren Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques Remote Sensing mining strata movement law mining ground fissure UAVs automatic identification Shenfu coalfield |
| title | Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques |
| title_full | Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques |
| title_fullStr | Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques |
| title_full_unstemmed | Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques |
| title_short | Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques |
| title_sort | identification of ground fissure development in a semi desert aeolian sand area induced from coal mining utilizing uav images and deep learning techniques |
| topic | mining strata movement law mining ground fissure UAVs automatic identification Shenfu coalfield |
| url | https://www.mdpi.com/2072-4292/16/6/1046 |
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