An integrated waterborne seismic-electromagnetic acquisition system and its application in bedrock investigation for cross-river tunnelingLuo, Qiang; Yang, Dikun; Ma, Jinguo
doi: 10.1088/1742-6596/2895/1/012012pmid: N/A
Construction of cross-river tunnels requires an understanding of the geology beneath the riverbed, particularly the solidity of the bedrock that may often be compromised by the weathering process. High-efficiency geophysical technology is demanded before drilling and other intrusive verification. We develop an integrated seismic-electromagnetic acquisition system for towed waterborne surveys by combing the single-channel seismic and towed transient electromagnetic (FloaTEM). The system consists of one towing boat and two floating boats connected and traveling in a line. The front deck of the towing boat houses the transmitting and receiving unit of the seismic system, and the seismic sensors are attached to one side of the boat below the waterline. The back deck contains the boat engine and the control unit of the FloaTEM system, whose transmitter loop and receiver coils are mounted in the two floating boats behind the towing boat. The system was tested at a section of Shunde Waterway near Guangzhou, China. The entire survey of about 7.6 line-km was finished in about 1.3 hours at a speed of 6 km/h. The seismic data and EM inversion model are highly consistent in delineating the three major interfaces below the water surface: water-sediments, sediments-weathered bedrock, and weathered-unweathered bedrock. In addition, the EM method is particularly useful in highlighting the contrast between the weathered and unweathered bedrock because of the difference in water content. The integrated survey revealed that the weathered bedrock layer thickens from the north to the south along the planned tunneling line, which is also favorably verified by the drill holes on the river bank of both sides.
3-D inversion of gravity and magnetic data and its application in the study on the characteristics of magmatic rocks in the Yin’e Basin and adjacent areasWeiheng, Yuan; Xiaoqiang, Chen; Chang’an, Guo; Chutong, Chen; Lingxiao, Zhang
doi: 10.1088/1742-6596/2895/1/012056pmid: N/A
Magmatic rocks are crucial in studies of tectonic evolution and hydrocarbon generation and reservoir formation. To elucidate the distribution characteristics of subsurface rocks in the Yin’e Basin, the study utilizes the method of the weighted physical property inversion of random subdomains to conduct a three-dimensional inversion analysis of gravity and magnetic data. The findings indicate that magmatic rocks are predominantly located in the western and southeastern regions. Regional faults significantly influence the distribution of magnetic rocks in the Yin’e Basin. In the eastern part, magmatic rocks are generally larger and predominantly aligned in a northeast-southwest direction, whereas in the western part, they are oriented northwest-southeast, aligning with the tectonic framework of the study area. Intermediate-basic rocks are primarily distributed along fault belts and uplift zones, while acidic rocks are concentrated in depression zones. These results provide substantial insights into the distribution patterns of magmatic rocks and hold significant implications for oil and gas geological exploration in the Yin’e Basin.
Physical simulation study of fracturing monitoring of hot dry rock by borehole-surface electromagnetic method in frequency domainXiaoqiang, Chen; Xiaoli, Mi; Hui, Cao
doi: 10.1088/1742-6596/2895/1/012053pmid: N/A
The Chinese mainland has abundant hot-dry-rock (HDR) resources. However, large-scale application is significantly challenged by the fracturing and stimulation of HDR reservoirs, making monitoring HDR fracturing essential. The borehole-surface electromagnetic method, which involves borehole excitation and surface collection of electromagnetic signals, captures stronger secondary field signals, aiding in assessing and guiding the fracturing effects in HDR reservoirs. Despite its potential, research on this method for HDR fracturing monitoring is limited. To explore and validate the effectiveness and mechanism of the frequency-domain borehole-surface electromagnetic method for HDR fracturing monitoring, this study conducts physical simulation experiments. The experiments use water to simulate HDR caprock, a mixture of water and sand for the HDR layer, and a graphite-cement anomaly model for the HDR fractured zone. Results indicate significant differences in electric field response amplitudes before fracturing, during fracturing, and after fracturing. When excited below and at the center of the anomaly model, the Ey response amplitude decreases with lower model resistivity, and when excited at the center, the Ey response amplitude similarly decreases with reduced resistivity. These findings demonstrate that the borehole-surface electromagnetic method effectively reflects changes in resistivity within the fracturing anomaly model, making it a suitable electromagnetic exploration method for monitoring HDR fracturing.
Urban Road Underground Defects Detection Using the Resistivity Method with an Irregular ArrayJiulong, Cheng; Zhongzhong, Xu; Yuqi, Zhang; Honpeng, Zhao; Yangchun, Han
doi: 10.1088/1742-6596/2895/1/012027pmid: N/A
While the resistivity method offers clear advantages for detecting underground defects beneath urban roads, the complex surface conditions of these roads preclude the deployment of regular arrays. This complexity significantly challenges conventional resistivity detection methods for underground defects. This study designed a high-density resistivity detection observation system adaptable to the deployment of irregular arrays along urban roads. This method obtains high-dimensional apparent resistivity data and employs Principal Component Analysis (PCA) for its dimensionality reduction. It introduces an improved position update method within the Chimpanzee Optimization Algorithm (ChOA), combined with Extreme Learning Machine (ELM), to propose a PCA-ChOA-ELM combination algorithm for inversion. This is then compared with BP (Back Propagation Neural Network), GABP (Genetic Algorithm optimized Back Propagation Neural Network), and ELM. To further test the PCA-ChOA-ELM resistivity inversion’s effectiveness in detecting underground defects beneath urban roads with an irregular array, a geological-geophysical model of underground defects was established. Numerical and physical experiments were combined to perform inversion imaging on models of underground defects, both with and without water. The research results show that the high-density resistivity method based on PCA-ChOA-ELM with an irregular array can adapt to the detection of underground defects under urban roads, can better reflect the electrical characteristics, location, and distribution of underground defects under urban roads, and achieve efficient and precise positioning of underground defects under urban roads.
Self-potential signal processing based on NMFZou, JinFeng; Cui, Yi-an; Xie, Jing
doi: 10.1088/1742-6596/2895/1/012023pmid: N/A
In recent years, new algorithms have been continuously applied in the field of geophysical data processing, all of which have achieved good results. However, there is currently no dedicated signal separation method for self-potential field signal processing. In this paper, we propose a self-potential signal separation algorithm based on non-negative matrix factorization (NMF) to perform blind source signal separation. We aim to separate different self-potential signals from the collected mixed signals, laying the foundation for subsequent work such as feature recognition. We utilized analytical formulas of simple polarization bodies and forward modeling procedures to generate a series of self-potential signal data. Subsequently, we conducted numerical simulation experiments for signal separation. The numerical simulation results demonstrate that the proposed algorithm is capable of separating self-potential signals of different models from mixed signals.
Analysis of the performance of frequency-Bessel transformation surface wave tomography method in geotechnical engineering investigationChai, Haibin; Han, Sixu; Luo, Qiang; Shao, Kui; Wen, Yonghui; Yang, Zhentao; Gao, Yuqiu
doi: 10.1088/1742-6596/2895/1/012044pmid: N/A
The frequency-bessel transform surface wave tomography method has emerged as a novel method for surface wave imaging during the past few years. This technique facilitates imaging large-scale crust-mantle structures and enables shallow engineering exploration. Compared to conventional methods like SPAC, it markedly enhances the quality of the surface wave dispersion curve, particularly in higher mode, enhancing the reliability of inversion. Its active and passive source data processing integration is particularly noteworthy, significantly broadening surface wave methods’ application scope. Our research reviews the performance of this technique in addressing common geological issues encountered in geotechnical engineering investigation, such as bedrock surface detection, cover layer detection, karst detection, and permafrost layer detection. Moreover, it recommends this innovative technique’s widespread adoption and further advancement.
The Morphological Features of the Qitianling Granitic Pluton: Constrains from Magnetotelluric Data in Southern HunanZhou, Keke; Liu, Jianxin; Cao, Li; Guo, Rongwen; Liu, Rong; Cao, Chuanghua; Zou, Guangjun
doi: 10.1088/1742-6596/2895/1/012042pmid: N/A
As one of the most important granites within southern Hunan, the Qitianling pluton controlled the formation and distribution of a series of polymetallic deposits, including Furong Sn deposit, Huangshaping Pb-Zn deposit, and Xintianling W-Mo deposit. Therefore, a further knowledge of morphological features of this pluton that can lead to a better understanding of development and evolution of deposits system of southern Hunan. In this study, we obtained the 3-D electrical resistivity model of the Qitianling pluton and its surrounding area based on 25 MT data, distinctly imaging the subsurface extent of this pluton. The results indicated that the Qitianling pluton exhibited an prominent high-resistance background (>1000 Ω.m), with a downward convexity extending to a depth of ∼ 5 km.
Comparative analysis of distributed acoustic sensing responses across different optical fiber types for engineering geological explorationLi, Zhongzhi; Liu, Bin; Fang, Gang; Cao, Hongyi; Zhao, Yang; Chen, Lei
doi: 10.1088/1742-6596/2895/1/012018pmid: N/A
Distributed Acoustic Sensing (DAS) has emerged as a revolutionary technology in seismic exploration, offering a novel approach to data acquisition. Despite its widespread adoption, the deployment of DAS in surface engineering exploration faces significant challenges, particularly concerning signal-to-noise ratio limitations and spatial resolution constraints. We conduct a comparative analysis of DAS responses using various types of optical fibers commonly employed for sensing purposes. We evaluate fibers of type A (tight-buffered fiber and strain-sensitive fiber) and type B (communication optical fiber, loose tube optical fiber, and temperature measurement optical fiber), as well as fibers in a helical configuration with different armoring materials. The deployment of optical fibers for this comparison includes both surface layouts and borehole integrations. Conventional geophones and hydrophones serve as benchmarks to contextualize the analysis and validate the performance of the DAS system data. This comparative approach highlights the utility of DAS for engineering seismic prospecting.
USCNet: Seismic P-wave arrival time pickup based on deep learningXu, Lei; Shen, Tong; Jiang, Xuan
doi: 10.1088/1742-6596/2895/1/012051pmid: N/A
The accurate pickup of seismic P-waves has important application value for real-time seismic data processing such as earthquake activity monitoring and earthquake disaster warning. However, existing low signal-to-noise ratio seismic data pickup algorithms often face problems of low accuracy and poor robustness. To address these issues, we designed a seismic P-wave picking network called USCNet. This network combines the advantages of U-Net and SegNet networks. The U-Net network performs well in picking accuracy, but requires a large number of learning parameters; SegNet networks are trained with fewer learning parameters, but have lower picking accuracy. The USCNet network integrates the CBAM attention mechanism, which can deeply extract key information from seismic data, thereby significantly improving the picking accuracy. We use the Stanford Earthquake Dataset (STEAD) as the data source. The experimental results show that USCNet performs significantly better than U-Net and SegNet networks on this dataset, with significant improvements in metrics such as Mean (μ), Variance (δ), Precision (P), and Recall (R), as well as significantly improved picking performance.
Advancing Offshore Wind Farm Site Assessments in Guangxi using Single-Channel Seismic MethodWei Min, P; Yi Gen, W; Song Chuan, X; Hu, B; Zhang, Y; Li Zheng, S; Xiao Li, J
doi: 10.1088/1742-6596/2895/1/012003pmid: N/A
This study explores the application of the single-channel seismic (SCS) method for offshore wind farm site assessments in China’s Beibu Gulf region, addressing the limitations of traditional drilling through an innovative data processing workflow. Given the increasing global demand for renewable energy and China’s leadership in offshore wind capacity, there’s a significant push towards exploiting the Beibu Gulf’s wind resources. However, the area’s complex geological challenges, such as thick sand layers and significant stratification differences, necessitate advanced exploration techniques. This research introduces a tailored data processing workflow comprising signal-to-noise ratio enhancement, swell correction, multiple wave suppression, amplitude compensation, and layer identification. This approach aims to reduce exploration costs, minimize environmental impacts, and support the strategic expansion of offshore wind energy in geologically challenging environments. The findings indicate a promising avenue for enhancing the accuracy and efficiency of offshore wind farm site assessments, contributing to the sustainable development of renewable energy resources.