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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Main Authors: Wei Zhou, Lichao Nie, Fahe Sun, Xinji Xu, Yi Zhang
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2020/8947591
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spelling 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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