干旱区生态环境知识资源中心(Arid): Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning

Arid

DOI	10.3390/app12020639
	Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning
	Hung, Yin-Chun; Zhao, Yu-Xiang; Hung, Wei-Chen
通讯作者	Hung, YC (corresponding author)，Natl Quemoy Univ, Dept Civil Engn & Engn Management, Kinmen 89250, Taiwan.
来源期刊	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2022
卷号	12 期号:2
英文摘要	Featured Application Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. Kinmen Island was in a state of combat readiness during the 1950s-1980s. It opened for tourism in 1992, when all troops withdrew from the island. Most military installations, such as bunkers, anti airborne piles, and underground tunnels, became deserted and disordered. The entries to numerous underground bunkers are closed or covered with weeds, creating dangerous spaces on the island. This study evaluates the feasibility of using Electrical Resistivity Tomography (ERT) to detect and discuss the location, size, and depth of underground tunnels. In order to discuss the reliability of the 2D-ERT result, this study built a numerical model to validate the correctness of in situ measured data. In addition, this study employed the artificial intelligence deep learning technique for reprocessing and predicting the ERT image and discussed using an artificial intelligence deep learning algorithm to enhance the image resolution and interpretation. A total of three 2D-ERT survey lines were implemented in this study. The results indicate that the three survey lines clearly show the tunnel location and shape. The numerical simulation results also indicate that using 2D-ERT to survey underground tunnels is highly feasible. Moreover, according to a series of studies in Multilayer Perceptron of deep learning, using deep learning can clearly show the tunnel location and path and effectively enhance the interpretation ability and resolution for 2D-ERT measurement results.
英文关键词	Electrical Resistivity Tomography (ERT) deep learning underground tunnel
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000760441900001
WOS关键词	ACTIVE FAULTS ; GPR ; SURFACE ; AREA ; ERT ; DAM
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376641
作者单位	[Hung, Yin-Chun; Hung, Wei-Chen] Natl Quemoy Univ, Dept Civil Engn & Engn Management, Kinmen 89250, Taiwan; [Zhao, Yu-Xiang] Natl Quemoy Univ, Dept Comp Sci & Informat Engn, Kinmen 89250, Taiwan
推荐引用方式 GB/T 7714	Hung, Yin-Chun,Zhao, Yu-Xiang,Hung, Wei-Chen. Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning[J],2022,12(2).
APA	Hung, Yin-Chun,Zhao, Yu-Xiang,&Hung, Wei-Chen.(2022).Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning.APPLIED SCIENCES-BASEL,12(2).
MLA	Hung, Yin-Chun,et al."Development of an Underground Tunnels Detection Algorithm for Electrical Resistivity Tomography Based on Deep Learning".APPLIED SCIENCES-BASEL 12.2(2022).