Advanced Geological Prediction

Shi, Shaoshuai; Xie, Xiaokun; Wen, Zhen; Bu, Lin; Zhou, Zongqing; Song, Shuguang; Zhao, Ruifeng

doi:10.5772/intechopen.88406

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The tunnel advanced prediction method is aimed at determining blind faults, karsts, and other geologic bodies ahead of the working face [1]. Several types of geophysical prospecting methods, including seismic tomography [2], reverse time migration [3], direct current electric sounding [4], magnetic resonance sounding [5], and the transient electromagnetic (TEM, also called time-domain electromagnetic, TDEM) method [6], have been applied to tunnel advanced prediction.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Fu,

Yang,

Zhou

et al. 2023

Geofluids

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The transient electromagnetic (TEM) method has long been applied in tunnel advanced prediction. However, it remains questionable to what extent a geologic anomaly body will influence the induced electromagnetic response in front of the heading face. The dilemma is partly because observed TEM data are frequently interpreted by empirical formulas or proportional relationships, and a quantitative measurement has not been established. In this paper, we strive to understand the TEM characteristics from a 3D finite-element time-domain (FETD) modeling aspect. The modeling algorithm is based on unstructured space meshing and unconditional stable time discretization, which ensures its accuracy and stability. The modeling algorithm is verified by a half-space model, in which the misfit of late-time channels that we are concerned with is generally below 1%. The algorithm has also been utilized to carry out the TEM response of tunnel models with different types of TEM devices. Through model studies, we find that both the traditional central-loop device and the recently developed weak-coupling opposing-coil device are feasible in tunnel advanced detection. Nevertheless, the latter type of device better distinguishes low-resistivity anomalies at 30 m ahead of the heading face with a relative difference (between models with and without the anomaly) of more than 1000% at certain time channels, compared with only a 10% difference of the central-loop device. Also, we conclude that the vertical electromagnetic field component should be recorded and interpreted together with the horizontal field to provide more convincing results.

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Section: Introductionmentioning

confidence: 99%

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Fu,

Yang,

Zhou

et al. 2023

Geofluids

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“…Advanced geological prediction can obtain the location and scale of water-rich areas in front of the tunnel face in advance and provide reasonable excavation methods and effective pre-support and pre-reinforcement measures for construction to reduce the possibility of a tunnel water inrush disaster [5][6][7]. It is one of the most important and basic means in current tunnel construction technology.…”

Section: Introductionmentioning

confidence: 99%

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

Ding

Yang

et al. 2022

Remote Sensing

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Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method.

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Advanced Geological Prediction

Cited by 2 publications

References 11 publications

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

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