Locating scatterers while drilling using seismic noise due to tunnel boring machine

Harmankaya, U.; Kaslilar, A.; Wapenaar, Kees; Draganov, Deyan

doi:10.1016/j.jappgeo.2018.03.017

Cited by 13 publications

(12 citation statements)

References 34 publications

(48 reference statements)

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“…If sufficient advance geological information is available, 6,7 risk mitigation measures can be enacted, which will fundamentally eliminate geological hazards and realize the optimal TBM excavation. [8][9][10] The advance geological information can be used to effectively avoid the loss of manpower, material resources, and construction time caused by geological disasters during excavation. However, due to the large depths, difficult sampling, and complex physical properties of geology samples, it is difficult to obtain this information.…”

Section: Introductionmentioning

confidence: 99%

“…Petronio et al 13 captured the vibration noise of the TBM using an acceleration and a seismic wave sensor and processed the data using the accumulation method to predict geological change zones ahead of the tunnel face. Inspired by the correlation between seismic wave interference and environmental noise, Harmankaya et al 9 proposed a method to predict isolation zones ahead of the tunnel face using TBM vibration noise. Lee et al 14 proposed a method of electrical resistivity tomography surveying which could predict abnormal strata ahead of the tunnel face through a comparison of electrode array data with numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

Huo

Guopeng

Liu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Geological layers excavated using tunnel boring machines are buried deeply and sampled difficultly, and the geological behavior exhibits high diversity and complexity. Excavating in uncertain geology conditions bears the risks of excessive damage to the equipment and facing geologic hazards. Many scholars have used various signals to predict the advance geology conditions, but accurate prediction of these conditions in real-time and without effecting operations has not been realized yet. In this article, based on a large amount of corresponding data, an advance prediction model of the rock mass category (RMC) is formulated. First, the problem is divided into two parts, which are modeled separately to reduce the complexity of design and training. Then, the two models are combined in a pre-trained model, which is retrained to as the final prediction model to avoid the problem of error accumulation. The final model can predict the advance RMC in real-time and without affecting operations. The accuracy of the prediction model reaches 99% at an advance time of 60 min. The advance RMC can be used to guide the selection of support modes and control parameters without additional detection equipment and excavation down-time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

Huo

Guopeng

Liu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Through scattered waves, one can investigate shallower or deeper parts of the subsurface depending on the scale of the study, whether it is local or global. For engineering‐scale studies, investigation of the near surface is often crucial for detecting and monitoring structures that cause scattering of body and/or surface waves (Campman and Riyanti, 2007; Xia et al ., 2007; Kaslilar, 2007; Almuhaidib and Toksöz, 2015; Tremblay et al ., 2017; 2018; Borisov et al ., 2018; Harmankaya et al ., 2018; Anjom et al ., 2019). Surface waves are considered to be useful for near‐surface investigations due to their relatively high signal‐to‐noise ratio that dominates the shallow seismic wavefield (Pan et al ., 2019; Gao et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling 3D elastodynamic wave scattering due to density and Lamé parameter contrasts of near‐surface scatterers

Harmankaya

Kaslilar

2022

Near Surface Geophysics

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Investigating the near-surface structure of the Earth through geophysical methods is a crucial aspect in many areas of study in geotechnical engineering, environmental science and exploration. Among several geophysical methods, seismic-based ones are widely used for characterizing near-surface features that are distinguished by contrasts in elastic parameters. In this paper, we model 3D elastodynamic wave propagation and scattering using a method based on domain-type integral representation with Born approximation. We calculate the scattered wavefield by considering the first-order perturbations in density and Lamé parameter contrasts of scatterers. Contrasts in Lamé parameters can be useful for determining the material properties of subsurface structures in cases of weak contrasts in velocities accompanying considerable Lamé parameter variations. We examine the effects of each parameter contrast on a series of models involving a subsurface scatterer. We also compare the seismograms obtained from our method with those from a 3D finite-difference wavefield modelling program, where we observe good agreement between the modelling results. Sensitivity of the wavefield to the perturbation in each model parameter is also examined by calculating and analysing the Fréchet derivatives. In general, the method discussed here can provide a solid foundation for prospective imaging studies involving density and Lamé parameters simultaneously.

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“…During tunnel excavation processes, it is possible to encounter adverse geological phenomena, such as faults, karst caves and (e.g. Inazaki et al, 1999;Neil et al, 1999;Zhao et al, 2006;Petronio et al, 2007;Shang et al, 2012;Guo and Luo, 2014;Li et al, 2017;Harmankaya et al, 2018). Wavefields in tunnel spaces are known to be very complex.…”

Section: Introductionmentioning

confidence: 99%

“…These methods include tunnel seismic prediction, tunnel seismic tomography, horizontal seismic profiling, true ref1ection tomography, tunnel‐seismic while drilling, land sonar and so on (e.g. Inazaki et al ., 1999; Neil et al ., 1999; Zhao et al ., 2006; Petronio et al ., 2007; Shang et al ., 2012; Guo and Luo, 2014; Li et al ., 2017; Harmankaya et al ., 2018). Wavefields in tunnel spaces are known to be very complex.…”

Section: Introductionmentioning

confidence: 99%

Vector P‐ and S‐wave pre‐stack reverse‐time migration for tunnel spaces

Luo

Zhu

et al. 2020

Near Surface Geophysics

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Migration is considered to be a key step in the data processing of the seismic advancement predictions of tunnels, and it directly affects the final interpretations. Therefore, with the goal of addressing the limitations of the current acoustic reverse-time migration processes, as well as deepening the understanding of the interactions between different wave modes in traditional elastic reverse-time migration, a vector P-and S-wave reverse-time migration method is proposed in this study. Consideration is given to the actual tunnel spaces, and forward calculations and reverse-time extrapolations of the wavefields are carried out based on the first-order velocity-stress elastic wave equation of P-and S-wave separation and a high-order staggered-grid algorithm. The imaging conditions of a source-normalized cross-correlation are used to realize the imaging. Then, a Laplace filtering method is adopted to suppress the low-frequency artefacts. Numerical simulations of the weak interlayer and karst cave models show that the proposed method had the ability to more effectively suppress noise and achieve highprecision imaging of tunnel spaces, when compared with using horizontal or vertical components to directly obtain the images. The positions and shapes within the tunnel spaces can be accurately imaged regardless of whether they are lithologic interfaces or local geological bodies. This is found to improve the resolution and accuracy of the tunnel advancement predictions. Finally, the proposed method is successfully applied to the real data processing, and the effects are found to be satisfactory.

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Locating scatterers while drilling using seismic noise due to tunnel boring machine

Cited by 13 publications

References 34 publications

Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

Advance prediction method for rock mass stability of tunnel boring based on deep neural network of time series

Modelling 3D elastodynamic wave scattering due to density and Lamé parameter contrasts of near‐surface scatterers

Vector P‐ and S‐wave pre‐stack reverse‐time migration for tunnel spaces

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