Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China
The seismic ahead-prospecting method is useful to detect anomalous zones in front of the tunnel face. However, most existing seismic detection method is designed for drilling and blasting tunnel. The detection method should be improved to satisfy the rapid tunneling of Tunnel Boring Machines (TBMs)....
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2020-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/8947591 |
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doaj-9bd47c6a73e04d95be1cae14ea0378b62020-11-25T03:56:34ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/89475918947591Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, ChinaWei Zhou0Lichao Nie1Fahe Sun2Xinji Xu3Yi Zhang4Geotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, ChinaGeotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, ChinaShandong Hi-Speed Group Cp., Ltd., Jinan, Shandong 250061, ChinaGeotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, ChinaGeotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, ChinaThe seismic ahead-prospecting method is useful to detect anomalous zones in front of the tunnel face. However, most existing seismic detection method is designed for drilling and blasting tunnel. The detection method should be improved to satisfy the rapid tunneling of Tunnel Boring Machines (TBMs). This study focuses on reducing the time spent on seismic data processing and result analysis. Therefore, to reduce the data processing time, an automatic initial model establishment method based on surrounding rock grade is proposed. To reduce the time spent on result analysis and avoid subjective judgment, a modified k-means++ method is adopted to interpret the detecting results and extracting anomalous zones. The efficacy of the developed method is demonstrated by field tests. The fractured zones such as cavity collapse and fissure are successfully predicted and identified.http://dx.doi.org/10.1155/2020/8947591 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Zhou Lichao Nie Fahe Sun Xinji Xu Yi Zhang |
spellingShingle |
Wei Zhou Lichao Nie Fahe Sun Xinji Xu Yi Zhang Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China Mathematical Problems in Engineering |
author_facet |
Wei Zhou Lichao Nie Fahe Sun Xinji Xu Yi Zhang |
author_sort |
Wei Zhou |
title |
Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China |
title_short |
Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China |
title_full |
Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China |
title_fullStr |
Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China |
title_full_unstemmed |
Automatic Approach for Fast Processing and Data Analysis of Seismic Ahead-Prospecting Method: A Case Study in Yunnan, China |
title_sort |
automatic approach for fast processing and data analysis of seismic ahead-prospecting method: a case study in yunnan, china |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
The seismic ahead-prospecting method is useful to detect anomalous zones in front of the tunnel face. However, most existing seismic detection method is designed for drilling and blasting tunnel. The detection method should be improved to satisfy the rapid tunneling of Tunnel Boring Machines (TBMs). This study focuses on reducing the time spent on seismic data processing and result analysis. Therefore, to reduce the data processing time, an automatic initial model establishment method based on surrounding rock grade is proposed. To reduce the time spent on result analysis and avoid subjective judgment, a modified k-means++ method is adopted to interpret the detecting results and extracting anomalous zones. The efficacy of the developed method is demonstrated by field tests. The fractured zones such as cavity collapse and fissure are successfully predicted and identified. |
url |
http://dx.doi.org/10.1155/2020/8947591 |
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