Lei Chen

Passive surface wave exploration using urban high-frequency noise has been studied extensively. As the main noise source of urban environment, vehicle noise can generate higher frequency surface waves, thus enhancing detection resolution. However, the existing methods usually hope to obtain more stable dispersion imaging results by collecting ambient noise for a longer period, resulting in high acquisition costs. To further enhance the efficiency of geological surveys, we propose a method that utilizes vehicle noise coda waves for geological investigations. We conducted numerical simulations of vehicle noise and proposed a method for calculating segment duration. We successfully captured the tail waves of vehicle noise and obtained high-quality surface wave signals through phase-weighted stacking. This method can obtain reliable dispersion curves by utilizing very short-duration vehicle noise. The vehicle noise from Jinan Metro Line R3 and Qingdao Metro Line 6 are collected, and data processing is conducted using the method proposed in this paper. The results show that this method can obtain the underground dispersion curve by using 10 s or even a few seconds of data, and has the advantages of higher mode surface waves are more developed and strong resistance to interference noise. The validity and reliability of the proposed method were verified by comparing the results obtained using this method with those obtained from traditional methods and geological data. It provides a means for rapid and accurate on-site investigation by utilizing vehicle noise with ultra-short durations.

Water inrush has become one of the bottlenecks restricting tunnel construction. Among various advanced forecasting techniques, the direct current method is more cost-effective and sensitive to water-bearing structures. It has been widely used in exploring water inrush disasters in practical engineering. Although traditional resistivity linear inversion methods are reasonably practical, they usually suffer from volume effects and cannot accurately locate the location and morphology of water-bearing bodies. Therefore, nonlinear techniques such as deep learning have recently become popular to directly approximate the inversion function by learning the mapping of apparent resistivity data to the geoelectric model. This work presents a novel deep learning-based electrical approach that combines resistivity and polarizability to estimate water-bearing location and morphology. Specifically, we design an encoder-decoder network. A shared encoder extracts features from the input data, two encoders output resistivity, and polarizability models, respectively, and fine-tuned collinear regularization for both outputs reduces solutions’ multiplicity. Compared with traditional linear inversion methods and independent parameter inversion, our proposed joint inversion method shows superiority in locating and delineating anomalous bodies.

There are many issues affecting multi-hole parallel tunneling and long tunnel ventilation, such as insufficient fresh air supplied at the tunnel face, pollution by contaminated winds, and uncoordinated work among multiple fans. In view of these issues, this paper uses the numerical simulation software Fluent combined with the orthogonal analysis test method, and, based on the actual working conditions of Yuelongmen Tunnel on the Chenglan Line, simulates the parameters of the duct, the installation position of the jet fan at the cross channel, and the combined operation status of multiple fans. The main conclusions are as follows: (1) the primary factor affecting the CO discharge from the tunnel face is the distance between the exit of the duct and the tunnel face, and the secondary factor is the diameter of the duct, while the position of the duct is the least influential factor; (2) the jet fan at the intersection of the cross channel should be set close to the pollution source in such a way as to prevent the jet from hitting the windward side of the cross channel and thereby avoid causing the contaminated wind to contaminate the adjacent tunnel; (3) in tunnels in which blasting operations do not occur, the simultaneous use of high-power air supplies and jet fans will reduce the air pressure in the tunnel, generate an air pressure difference with the adjacent tunnel, trap polluted air, and reduce the ventilation effect.

Predictive filtering methods are widely used in industry to remove random noise, owing to their stability and efficiency. Traditional predictive filtering methods use a fixed autoregressive order (filtering factor) in the spatial direction. However, denoising spatially varying seismic data have thus far been ineffective. In this study, the effect of nonstationary seismic data variations is overcome using local windowing or nonstationary denoising methods. We propose a nonstationary predictive filtering method in which an autoregressive model is constructed using spatially varying filtering factors. First, we evaluate the structural complexity of the global data based on a local window selection using plane-wave destruction. Then, adaptive filtering factors are proposed depending on the structural complexity of the seismic data. Finally, the autoregressive model containing the adaptive filtering factors is solved. We use three quality measures to evaluate the denoising performance: the signal-to-noise ratio ( $S/N$ ), the peak signal-to-noise ratio (PSNR), and local similarity (LS). The proposed method acts on both synthetic and field data, including both pre- and post-stack seismic data. Compared with prior nonstationary filtering methods, experimental results demonstrate that structural complexity-guided (CG) predictive filtering enables efficient random noise attenuation and reduces signal leakage.

To better understand the shallow sea geological information and avoid the risk caused by potential geo‐disasters, the efficient offshore geological exploration methods are required. Better detection resolution can be obtained by using a spark source. As the foundation, the observation system plays an important role for geological detection. The influence of the variation of the parameters of the observation system on the detection accuracy is analysed theoretically. Then, the numerical simulations based on the finite difference method was applied, and imaging characteristics of observation system with different parameter were studied. For submarine acoustic exploration, the spark source with frequency over 200 Hz can obtain the clear reflections for geological interpretation; besides, the receiver array with the interval of 5 m–10 m helps to obtain better wave signals when the buried depth of a geological body is less than 300 m, and the width is more than 10 m. Based on numerical simulations, the observation system was optimized and designed. The results of numerical examples show that the accurate position information of the structure can be obtained by using the observation system proposed in this paper. Different imaging performances are obtained by adjusting the parameters of the observation system. On this basis, combined with directivity analysis, the optimal observation system parameters are proposed. Finally, the proposed observation system is used to image the fault model. The research results of this paper can provide reference for the observation system design in similar projects.

Seismic method is a major approach for detecting the seismic geological features ahead of the tunnel, understanding the distribution of unfavorable geology, and ensuring the safety of tunnel construction. Observation system is the key for seismic detection, many studies have been conducted to optimize the observation system; however, most of them focused on the surface seismic investigation and numerical simulation rather than in tunnel field environment (limited aperture and full space environment). How to obtain better wavefield information with limited observation aperture is a great challenge. In this study numerical simulation and instrumental techniques (GPR, DC, etc.) were implemented to further check the result of seismic detection at the 1# tailrace tunnel at the Wudongde hydropower station. In the field case, observation detectors were arranged spatially in the tunnel and source points were placed in four ways: linearly along a single side, on the tunnel face, in front of the detectors, and behind the detectors. Then, after data acquisition, the data processing is conducted to carry out the migration results. The imaging results indicate that the observation system with sources and detectors in liner arrangement (with equal interval) helps to suppress artifacts, further supporting the advantages of spatial observation system with liner observation line (detectors). Moreover, the study provides suggestions for geological prospecting in similar tunnel projects.