Application of Cross-borehole Integrated Geophysical Methods for the Detailed Investigation of Karst in Urban Metro Construction

2019 ◽  
Vol 24 (4) ◽  
pp. 525-536
Author(s):  
Jun Zhang ◽  
Shengdong Liu ◽  
Qinghua Chen ◽  
Bo Wang ◽  
Chuan Ren
Keyword(s):  
High Precision ◽  
Geophysical Methods ◽  
Ground Subsidence ◽  
Small Scale ◽  
Tunnel Construction ◽  
Detection Accuracy ◽  
Geophysical Field ◽  
Geological Conditions ◽  
Fracture Development ◽  
Intercity Railway

With the significant development of China's metro construction, the development of urban underground karst poses a serious threat to related tunnel construction and public safety, with frequent occurrences of mud and water inrushes during tunnel construction and urban ground subsidence events. Because of the complex, urban, and shallow geological conditions and construction environments, conventional geophysical methods cannot meet the requirements for high-precision detection of small-scale and inhomogeneous complex geological bodies. Based on numerical simulation, herein we comprehensively applied both cross-borehole electrical resistivity tomography (ERT) and cross-borehole seismic computed tomography (CT) to urban underground karst surveys of the Hangzhou-Fuyang intercity railway. The results showed that: 1) under limited urban construction conditions, the use of advanced geophysical monitoring equipment greatly improved construction efficiency; 2) utilizing drilling geological results to calibrate the abnormal geophysical field attribute parameters (including wave velocity and resistivity) improved the accuracy of karst exploration and reduce defective geophysical multi-explanation effects; 3) applying the joint comparative explanation of both velocity and resistivity profiles can distinguish and explain karst and fracture development zones; 4) 550 pairs of velocity and resistivity profiles were obtained which revealed 258 karst cave anomalies and 5 fracture development zones which integrated detection accuracy exceeded the 1 m level. Thus, the high-precision joint cross-borehole tomography technology was shown to be useful for guiding intercity railway construction.

Imaging the Geology Ahead of a Tunnel Using Seismics and Adaptive Polarization Analysis

2020 ◽  
Vol 25 (2) ◽  
pp. 189-198
Author(s):  
Lei Chen ◽  
Chao Fu ◽  
Xinji Xu ◽  
Lichao Nie
Keyword(s):  
Time Window ◽  
Geophysical Methods ◽  
Weighting Coefficient ◽  
Tunnel Construction ◽  
Polarization Analysis ◽  
Polarization Direction ◽  
Tunnel Face ◽  
Seismic Method ◽  
Imaging Results ◽  
Geological Conditions

The seismic method is one of the main geophysical methods that are widely used to image the geology ahead of tunnels during tunnel construction. However, owing to the complex environment and limited observation aperture in a tunnel, symmetric false results (that appear in imaging results but not in the actual environment) frequently occur in imaging results. In a symmetric false reflection, false and true reflection points are axisymmetric around the tunnel axis. Such false results frequently cause errors in the interpretation of the geological conditions ahead of a tunnel face. To overcome this problem, a seismic method that uses adaptive polarization analysis was adopted to better image geological conditions. Based on an adaptive time window, the polarization characteristics of seismic signals were analyzed to calculate the main polarization direction. The symmetric false results in imaging results were suppressed by adopting a weighting coefficient based on the angle between the main polarization direction and ray direction. Numerical simulations revealed the superiority of the method when applied to synthetic data processing. Moreover, the method was applied to a diversion tunnel. The method successfully identified the fracture zones ahead of the tunnel face, thus significantly enhancing the safety of tunnel construction.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

Deformation Monitoring and Data Analysis for Metro Construction with Mining Method in Dalian

2013 ◽  
Vol 353-356 ◽  
pp. 1604-1608
Author(s):  
Guang Bin Bai ◽  
Jie Zhao ◽  
Li Sheng Liu
Keyword(s):  
Data Analysis ◽  
Construction Method ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Ground Subsidence ◽  
Tunnel Construction ◽  
Mining Method ◽  
Construction Process ◽  
Subway Tunnel ◽  
The Future

Based on a subway tunnel construction, the construction method was introduced. The ground subsidence, crown settlement and convergence displacement caused by the cut tunnel are monitored during the tunneling construction and the results of monitoring data for them are analyzed. This technology wells to guide the tunnel-entering construction effectively and avoid the tunnel-entering construction process prone to landslides, thus ensuring the safety of the tunnel construction and will guiding the future construction.

HE-YOLO: Aerial Target Detection Based On Improved YOLOv3

2021 ◽  
Author(s):  
Yuqing Zhao ◽  
Jinlu Jia ◽  
Di Liu ◽  
Yurong Qian
Keyword(s):  
Target Detection ◽  
Loss Function ◽  
Detection Rate ◽  
Aerial Image ◽  
Experimental Comparison ◽  
Small Scale ◽  
Detection Accuracy ◽  
False Detection Rate ◽  
False Detection ◽  
Bounding Box

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.

Experiment of 3D Seismic Reflection Technique for Forward Probing on TBM Tunnel Face

2019 ◽  
Vol 24 (4) ◽  
pp. 609-619
Author(s):  
Ao Song ◽  
Bin Song ◽  
Rongyi Qian
Keyword(s):  
High Resolution ◽  
Tunnel Boring Machine ◽  
Detection Accuracy ◽  
3D Seismic ◽  
Engineering Project ◽  
Tunnel Wall ◽  
Tunnel Face ◽  
Detection Depth ◽  
Imaging Results ◽  
Geological Conditions

Geophysical technologies are used to mitigate geological hazard caused by adverse geological conditions in front of a tunnel face. The prevailing method for forward probing for tunnels constructed by a tunnel boring machine (TBM) for advance prediction is based on seismic detection. Conventional tunnel seismic prediction technology uses P- and S-waves with sources fired on the tunnel wall or face and layout receivers on the tunnel wall to acquire the reflected waves. However, the results show that most of these methods have different deficiencies that are in either low detection accuracy, short detection depth, and/or multiplicity in imaging. This paper proposes a new high resolution tunnel advance prediction technology on the face based on 3D seismic wave detection. It arranges the 3D high-density source and recording geometry on the tunnel face to receive reflected P-waves for 3D imaging. By using the 3D numerical simulation, we first analyze the energy distribution and propagation characteristics of the wave field, which proves that our method is feasible. Compared with the conventional technologies, the seismic energy propagating towards the tunnel face is stronger and produces rich reflected information. The reflected wave has the advantages of bandwidth, strong energy and little interferences from surface wave, so that the seismic phases are easy to be identified. On this basis, we present the high resolution true 3D prediction technology to obtain more comprehensive and abundant azimuth information. Our approach is further validated by an application experiment in a real-world engineering project of water conveyance tunnel. The results show that the new technique has a greater detection length, higher detection accuracy and more reliable imaging results.

scholarly journals The theory and application of the random dislocation directional seismic wave technique in a tunnel environment

2019 ◽  
Vol 16 (5) ◽  
pp. 939-949
Author(s):  
Yonggao Yue ◽  
Tao Jiang ◽  
Jingye Wang ◽  
Yunfeng Chao ◽  
Qi Zhou ◽  
...  
Keyword(s):  
Seismic Wave ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Geological Structure ◽  
Detection Accuracy ◽  
Detection Distance ◽  
Geological Structures ◽  
Multiple Wave ◽  
Geological Conditions ◽  
Wave Technique

Abstract Performing exact predictions of geological conditions for tunnel construction is important for ensuring safe and quick tunnel engineering. Weak effective signals and strong random noise are the main factors that affect the distance and precision of tunnel seismic detection. Considering that directional seismic wave (DSW) technology has the ability to enhance target signals and suppress random noise, we attempt to apply this method to solve the problems of low detection accuracy and short detection distance. However, the process of data processing with the DSW technique generates false multiple wave interference (FMWI), which can lead to the misinterpretation of geological structures. This study analyses the origins of FMWI and presents the random dislocation directional seismic wave (RDDSW) method to suppress this interference. The results of a numerical simulation indicate that the FMWI is effectively suppressed and that the signal-to-noise ratio of the data is increased by approximately N times through use of the N-element RDDSW technique. In the ideal case, only spherical diffusion attenuation is considered, and the detection distance increases by approximately $\scriptstyle\sqrt N $ times. In addition, this method is also effective for signals from curved events, thereby improving the precision of the analysis of the geological structure of the tunnel. Furthermore, the field data results further verify that the RDDSW technique can significantly suppress interference and thus improve the quality of the data at little cost. Hence, the RDDSW technique has great significance for accurately predicting the geological structures of tunnels and increasing the detection distance in tunnels.

Research on Advanced Geological Forecast Technology in the Large and Complex Underground Projects

2014 ◽  
Vol 608-609 ◽  
pp. 756-760
Author(s):  
Hong Wen Zhou ◽  
Chun Ying Lei ◽  
Yi Jun Shang ◽  
Jian Feng Zhang ◽  
Wei Wei ◽  
...  
Keyword(s):  
Coming Out ◽  
Multiple Solutions ◽  
Construction Project ◽  
Geophysical Methods ◽  
Construction Process ◽  
Underground Construction ◽  
Forecast System ◽  
Geological Interpretation ◽  
Combination Forecasting ◽  
Geological Conditions

The multi-hazard, wide-covered, complex factors during the large complex underground construction process pose severe challenges to the construction project. With the help of successful forecast cases, typical geological interpretation signs, optimization of combination forecasting scheme, comprehensive geological forecast system is constructed to effectively solve the disadvantage of multiple solutions coming out from the single geophysical methods, and greatly improve the accuracy of forecast of adverse geological conditions, thus ,the target of safety, economical operation and efficiency is achieved.

scholarly journals Time-lapse gravity and levelling surveys reveal mass loss and ongoing subsidence in the urban subrosion-prone area of Bad Frankenhausen, Germany

Solid Earth ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 599-619 ◽  
Author(s):  
Martin Kobe ◽  
Gerald Gabriel ◽  
Adelheid Weise ◽  
Detlef Vogel
Keyword(s):  
Mass Loss ◽  
Soil Water ◽  
High Precision ◽  
Urban Areas ◽  
Quantitative Estimation ◽  
Monitoring Network ◽  
Time Lapse ◽  
Ground Subsidence ◽  
Special Focus ◽  
Precision Time

Abstract. We present results of sophisticated, high-precision time-lapse gravity monitoring that was conducted over 4 years in Bad Frankenhausen (Germany). To our knowledge, this is the first successful attempt to monitor subrosion-induced mass changes in urban areas with repeated gravimetry. The method provides an approach to estimate the mass of dissolved rocks in the subsurface. Subrosion, i.e. leaching and transfer of soluble rocks, occurs worldwide. Mainly in urban areas, any resulting ground subsidence can cause severe damage, especially if catastrophic events, i.e. collapse sinkholes, occur. Monitoring strategies typically make use of established geodetic methods, such as levelling, and therefore focus on the associated deformation processes. In this study, we combine levelling and highly precise time-lapse gravity observations. Our investigation area is the urban area of Bad Frankenhausen in central Germany, which is prone to subrosion, as many subsidence and sinkhole features on the surface reveal. The city and the surrounding areas are underlain by soluble Permian deposits, which are continuously dissolved by meteoric water and groundwater in a strongly fractured environment. Between 2014 and 2018, a total of 17 high-precision time-lapse gravimetry and 18 levelling campaigns were carried out in quarterly intervals within a local monitoring network. This network covers historical sinkhole areas but also areas that are considered to be stable. Our results reveal ongoing subsidence of up to 30.4 mm a−1 locally, with distinct spatiotemporal variations. Furthermore, we observe a significant time-variable gravity decrease on the order of 8 µGal over 4 years at several measurement points. In the processing workflow, after the application of all required corrections and least squares adjustment to our gravity observations, a significant effect of varying soil water content on the adjusted gravity differences was figured out. Therefore, we place special focus on the correlation of these observations and the correction of the adjusted gravity differences for soil water variations using the Global Land Data Assimilation System (GLDAS) Noah model to separate these effects from subrosion-induced gravity changes. Our investigations demonstrate the feasibility of high-precision time-lapse gravity monitoring in urban areas for sinkhole investigations. Although the observed rates of gravity decrease of 1–2 µGal a−1 are small, we suggest that it is significantly associated with subterranean mass loss due to subrosion processes. We discuss limitations and implications of our approach, as well as give a first quantitative estimation of mass transfer at different depths and for different densities of dissolved rocks.

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