bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-component Seismic Data Random Noise Attenuation via Quaternion Wavelet Transform
Authors: Li Fan , Gao Jianjun , Wang Yun , Chen Haifeng 
Published: December 2023

Abstract
In this paper, we present a new method with quaternion wavelet transform (QWT) to suppress the random noise of multi-component seismic data simultaneously. The proposed method could preserve the vector nature of the wavefield compared with the traditional wavelet threshold denoising approaches. Artificial data are used to test the new method’s performance. The experimental results show that QWT gets higher SNRs and better preserves weak reflecting energy.
Fast 3D Magnetotelluric Forward Modeling Based on a Regularization Approach in Spherical Coordinate
Authors: Wang Yongfei , Liu Jianxin , Guo Rongwen , Tian Shengqi , Li Jian 
Published: December 2023

Abstract
The earth’s curvature shouldn’t be ignored for the large-scale magnetotelluric (MT) modeling. A forward code of spherical coordinate MT method based on staggered grid finite difference method is developed. In addition, the forward problem is usually solved numerically, mostly based on iterative solution. Krylov subspace method is widely used to solve electromagnetic forward modeling problems in frequency domain. However, as the period increases, the convergence of these methods becomes significantly slower (especially for long-period MT data). We added a scaled grad-div operator to the double curl equation of the electric field to perform the nondivergence condition (CCGD). The results show that CCGD method is more efficient than other methods, which is an efficient method for our code.
Application of Impact Elastic Wave Method in Quality Inspection of Concrete Lining
Authors: Hu Xinmin , Wang Yanlong , Zhang Guoqiang , Du Lizhi 
Published: December 2023

Abstract
As the main part of inter-basin water transfer project, water tunnel plays an important role in long-distance water conveyance. In water tunnel, the lining quality problem is one of the important potential safety hazards of the tunnel engineering. It is very necessary to detect the internal defects of concrete lining. In this paper, a multi-parameter comprehensive interpretation method based on impact elastic wave is proposed to quickly detect the internal defects of lining. The common offset time domain analysis method, frequency domain analysis method and two-dimension Rayleigh wave method are used for data processing. The detection method has the characteristics of single data acquisition and multi-parameter joint interpretation. The physical model of concrete lining is tested and compared with the result of GPR method to verify the accuracy of this method. The coring results show the reliability and stability of this method in practical application.
The Comprehensive Detection Technology for Embankment Leakage
Authors: Cao Yu , Cheng Xinhai , Yue Mingxin , Liu Huizhou , Zhou Guanqun , Wu Xiaoping 
Published: December 2023

Abstract
Based on the study of geophysical characteristics of embankment leakage, this paper adopts Dual coil transient electromagnetic (Dual TEM) and parallel direct current resistivity method(PDC) to carry out comprehensive detection. Dual TEM can greatly reduce the shallow blind area of conventional transient electromagnetic and improve the resolution of induced signal, PDC method can efficiently collect signals of all electrical arrange and spontaneous potential signals, By using the cross-correlation processing technology between spontaneous potential and resistivity data. we quickly eliminate the false abnormal influence of low resistivity in embankment, it is a good application in the detection of Paihe embankment leakage in Hefei, Anhui Province in 2020.
Deep Learning for Inversion of Tipper Data of a Certain Railway Tunnel in Tibet Area
Authors: Yao Yu , Luo Bing , Zhang Zhihou , Shi Zeyu , Lu Runqi 
Published: December 2023

Abstract
The traditional inversion methods for tipper rely excessively on the selection of the initial model, whose global search capability is poor. Inspired by the significant approximation advantage of deep learning for nonlinear inverse problems with big data, we design a deep learning architecture called TipInv-net for the inversion of tipper. TipInv-net takes the improved U-net as the basic framework to obtain the tipper response characteristics of the abnormal body, and then, dense skip connection is applied among the nested standard convolution modules to alleviate the gradient disappearance problem and enhance feature propagation. It’s worth noting that we construct a feature pyramid of tipper response via average pooling to obtain multi-scale receptive fields, which, thus enhancing global and detail location of abnormal body. The theoretical model test indicates that the position and attitude of geological anomaly body can be distinguished via TipInv-net, even in the presence of a certain level of noise, the inversion accuracy will not be greatly affected. TipInv-net has strong generalization. Besides, after the training process of the network, we can obtain the inversion result immediately. In order to verify the effectiveness of the method in this paper, the inversion of the field tipper data of a section of a certain railway tunnel in Tibet area is carried out.
UAV Seismic Source Traveltime Tomography Technology in Steep Mountainous Areas
Authors: Zhang Jun , Qian Rongyi 
Published: December 2023

Abstract
Active source seismic exploration is one of the most important high-resolution methods to obtain information about underground structures in tunnel engineering, and the seismic source is the key equipment to acquire high-quality data. In steep mountainous areas with complex geological structures, surface undulation, and drastic changes, the use of traditional seismic sources with high cost can seriously affect the efficiency and accuracy of detection. In this paper, a survey experiment of tunnel engineering is carried out in Lijiang, Yunnan Province, completing a fast, high-quality seismic data acquisition through utilizing the convenient, environmentally friendly, and economical UAV (Unmanned Aerial Vehicle) seismic source as the shot and wireless node instrument as the receiver. The experiment shows that the UAV seismic source can achieve low-cost and environmentally friendly excitation of seismic waves in complex mountainous areas, making a breakthrough in seismic source equipment. We obtain a high-precision velocity model by traveltime tomography, identifying the thickness of the overburden layer in the study area, and providing an important reference for tunnel engineering.
Estimation of Groundwater in Tarim Basin from Three-dimensional Resistivity Model Constructed by Magnetotelluric Data
Authors: Li Sangsang , Xu Yixian , Yang Bo 
Published: December 2023

Abstract
Groundwater is an important resource of irrigation and domestic water in Tarim Basin (TB), the largest arid inland basin in China. Hydrogeologic models and hydrogeochemical analysis have provided information of groundwater flow, salinity and hydrochemical type in TB. However, there is lacking in quantitative estimation of groundwater volume due to that the underground aquifer structure is unclear. To address the problem, magnetotelluric (MT) data were collected in TB and subsurface electrical resistivity were produced by MT inversion. To estimate the porosity of aquifers by resistivity, we should determine the resistivity of groundwater which is controlled by the Total Dissolved Solids (TDS) in water. From the previously published data, TDS increases from piedmont plain to alluvial plain along groundwater flow direction in TB and other similar settings. In terms of spatial distribution of TDS and 3-D resistivity model, the effective porosity of aquifers in the study area was calculated. Finally, by taking account of the water table and thickness of Quaternary sediments collected by BGP Inc., the total groundwater volume was estimated in northern parts of TB.
Estimation of Freshwater and Salinity Interfaces in the Coastal Area of Jiangsu Province by Electrical Sounding
Authors: Xu Zhengwei , Wang Xuben , Zhang Qihua , Lu Juntao 
Published: December 2023

AbstractIn the coastal alluvial plain of the Yangtze River Delta, specifically the Nantong Region of Jiangsu Province, China, there is a heightened vulnerability of the groundwater aquifer area due to increased urbanization and seawater intrusion. This location, a rich ecological domain with an extended history of marine deposition, is predominantly influenced by saltwater intrusion that significantly determines the groundwater quality. It is situated on the third topographic ladder. To discern the spatial variations in the hydrogeological zonation and the fresh-saline divide, we put forth an innovative methodology that incorporates multiple forms of datasets. The findings derived are of considerable relevance to the understanding of groundwater distribution related to Quaternary geology and hydrogeology within the region of interest. Furthermore, these interpretive outcomes may be instrumental in informing and directing the management and utilization of water resources in the study area.
Three-dimensional Response Characteristics of Self-potential Field for Dam Leakage Hidden Danger
Authors: He Shangfu , Deng Juzhi , Liu Suiming , Chen Hui , Gao Jianglin , Wang Shuo , Chen Yufeng , Yu Hui , Zhang Guokun , Li Yan 
Published: December 2023

Abstract
In order to improve the application of self-potential field monitoring in detecting the potential reservoir dam leakage, we deduced the coupling equation of the self-potential field with water flow field and ions diffusion field, based on the formation mechanism of the self-potential field. We realized the three-dimensional numerical simulation of the self-potential field of reservoir dam by coupling finite and infinite elements by analyzing the response characteristics of the self-potential field with different water levels, different levels of danger, and different periods of typical dam leakage models. The results show that the magnitude of the self-potential field is proportional to the size of the leakage channel and the magnitude of water level difference. Thus, the distribution characteristics of the self-potential field could indicate the development period of a leakage to some extent, and the leakage location and early warning could be realized by monitoring self-potential fields.
Pronounced Changes of Multi-geophysical Parameters Associated with Maduo (M= 7.4) Earthquake
Authors: Ye Qing , Singh Ramesh P. , Wang Xiao , Tao Zhigang , Tang Lei , Yao Saisai , Ma Weiyu , Fan Ye , Fan Chunyan , Liu Gaochua , Liu Chunguo , Liu Yangyang 
Published: December 2023

Abstract
The China Earthquake Administration (CEA) has a large network for monitoring multiple geophysical parameters to forecast earthquake and to have early warning information about an impending earthquake. In this paper, we present the analysis of various geophysical measured and borehole parameters (resistivity, borehole strain, water level, water temperature, atmospheric temperature, etc.) within 400 km radius of the epicenter. The strain measured in the borehole show pronounced tensional and compressional changes, and the direction of the main compressive strain is found to rotate clockwise. The active compression of the whole Bayankara block is found to be affected, the resistivity near the fault began to decrease and increase in different directions. The water level observed at the Yushu water well located in the southwest from the epicenter is found to change in the opposite direction at the Huangyuan borehole located in the northeast from the epicenter. Yushu water well station is affected by the clockwise activity of the whole Bayankara block, at this water level increases from 2017 and begins to decline from August 2020. Such pronounced decline in water level far from the epicenter of the earthquake prior to the earthquake event provides an early warning of the Maduo earthquake. The decline of the water level is associated with the changes in the stress within the epicentral region, such changes could provide long-term precursory information about an impending earthquake.
Dynamic Monitoring of In-situ Chemical Oxidation for Remediation of Hydrocarbon Contamination with Electrical Resistivity Tomography
Authors: Xia Teng , Zhang Jiaming , Guo Lili , Li Shupeng , Ma Xinmin , Mao Deqiang 
Published: December 2023

Abstract
In situ chemical oxidation (ISCO) using persulfate, a conductive oxidant, provides a powerful signal for non-invasive geophysical techniques to characterize the remediation process of hydrocarbon contaminants. This study used ISCO for remediation in a laboratory sandbox and at an on-site contaminated site to evaluate the ability of resistivity chromatography (ERT) to monitor the process of alkali-activated persulfate remediation of hydrocarbon-contaminated soils. The experimental results show that the persulfate plume migrates mainly vertically due to density-driven flow. The relationship between resistivity and pollutant metrics further showed that pollutant depletion contributed 25.6% to the resistivity change in polluted source regions, while this contribution was less than 16% in low or non-polluted regions. In addition, field monitoring results indicate that the resistivity response is consistent with changes in water chemistry, water table, contaminant concentration and temperature. In conclusion, geophysical methods are excellent for dynamic monitoring of in situ remediation processes for hydrocarbon contamination.
Three-Dimensional Inversion Algorithm with A Footprint Computational Domain for Underground Engineering
Authors: Ou Jian , Zhou Hua , Ou Renwen , Li Jianyuan , Liu Chunming 
Published: December 2023

Abstract
Granite bodies pose a common geohazard in urban underground engineering. The GEM-2 system, employing small coils as transmitter and receiver (Tx-Rx), has gained widespread adoption for efficiently and non-invasively delineating low resistivity targets with 1D results in the near-surface. In this paper, we propose a 3D inversion algorithm based on the footprint-guided compact finite element method and Gauss-Newton optimization to accurately delineate the 3D structure of granite boulders. To demonstrate the effectiveness of our 3D inversion method, we conducted a GEM-2 test survey in a scenario featuring a granite boulder. The inversion results exhibit superior horizontal resolution compared to the current 1D inversion methods. The three-dimensional contour of the boulder is clearly depicted by areas of relatively high resistance, showcasing the capability of our inversion algorithm.
A Deghosting Method Based on Deep Learning
Authors: Zhao Yuetong , Li Yaxing , Jia Xiaofeng , Wu Xinming 
Published: December 2023

Abstract
Interference of ghost has a bad effect to marine seismic exploration, which reduces the resolution seismic data and accuracy of seismic interpretation. To solve these problems, we propose a deghosting method based on deep learning. The trial calculation of synthetic models shows that this method is more streamlined and efficient in deghosting, which has good adaptability to data with with different geological models, acquisition systems and signal-to-noise ratio, recovers the energy of the frequency band affected by the ghost notch.
A Method of Removing the Interference from Urban Railway Transit in The Geoelectric Field
Authors: Zheng Guolei , Ye Qing , Liu Jun , Du Xuebin 
Published: December 2023

Abstract
In recent years, with the development of urban railway transit (URT), interference in the geoelectric field (GEF) has been generated. This brings serious difficulties to the acquisition of earthquake precursors. In this study, by analyzing the spectral features of daily minute-sampling GEF data without and with interference from urban railway transit (URTI) and without one respectively, the Butterworth function was selected as a transfer function for Fourier filtering, and its cutoff frequency was set. A method of removing URTIs (RURTIs) was developed. Then, the RURTI method was used to process the NS, EW and NE GEF data to better recognize the anomaly before the Guye Ms 5.1 earthquake at Yanqing station. Compared with a polynomial-smoothing filtering method, the RURTI method has better performance in improving the signal-to-noise ratio and recognizing preearthquake anomalies. After the differences between the original GEF data and the processed data using the RURTI method were calculated, a method of recognizing the preearthquake anomaly was found.
Numerical Simulation of Wave Equation Using the Finite Difference Method with Variable Refined Grid in Complex Near-surface Condition
Authors: Zhang Zhijia , Sun Zhangqing , Han Fuxing , Wang Ruihu , Lu Jipu , Gao Zhenghui , Cen Wenpan , Liu Mingchen 
Published: December 2023

Abstract
The horizontal layered refined grid is adopted in the conventional finite difference method (FDM) of variable refined grid for the numerical simulation of wave equation. But, the method cannot well adapt to the characteristics of undulated topography and the variation of velocity structure in near-surface region. To solve this problem, a numerical simulation of wave equation using FDM with undulated variable refined grid is proposed in this paper. The grids are generated according to the undulated topography and the spatial distribution of velocity in the near-surface region from low-velocity to the high-velocity layer. For ensuring the accuracy while taking into account the computational efficiency, the FDM with variable coefficient in above grids is used to discretize the acoustic wave equation. The numerical evaluation and example show that the same accuracy as the method using fine grid can be obtained by the new method. However, its computational time is only 43.6% of the one spent by the fine grid. Furthermore, the new method can also stably adapt to the real complex loess plateau near-surface model in a working area of Ordos Basins.
Weighted Adjoint-state Crosshole Traveltime Tomography
Authors: Liu Liu , Li Shaojun , Shi Zhenming , Peng Ming , Wang Yao 
Published: December 2023

Abstract
The adjoint-state method of first arrival traveltime tomography, featured with implicit ray tracing calculation, was used to compute the gradient of the misfit function without introducing Fréchet derivatives. However, the majority of velocity gradients in the adjoint-state method focus on the high velocity area due to high raypath density. Thus, the current adjoint-state method faces challenges of imaging low velocity anomalies in the near-surface by crosshole seismic acquisition. In this paper, we proposed a weighted adjoint-state Eikonal equation-based crosshole traveltime tomography method. A preconditioning operator is provided to fix the ray path density influence. The weighted method was tested on a homogeneous model, a checkerboard model, and a reduced-scale Marmousi model. Both misfit cures and velocity logs are used to make a comparison between the weighted method and the traditional method. The results show the weighted method has better resolution, faster convergence speed, and lower final residual, which demonstrate the method can mitigate the ray path effect and reduce the inverted velocity error of low velocity anomalies.
A Simple Model Generator for Radar Sounding Applications Based on Stochastic Media Model and gprMax
Authors: Lv Chen , Hao Tong 
Published: December 2023

Abstract
Ground penetrating radar (GPR), or more generically called radar sounding, is an established technique for probing subsurface objects and sublayer structures. Simulating pre-constructed models and comparing with the real-world observations can contribute to retrieve the geometrical and physical properties of subsurface objects. However, most GPR simulations are limited to geometrically simple models with homogeneous physical properties of subsurface media which are generally unable to represent the real-world situations. In this paper, we develop a simple model generator by combining the stochastic media model and gprMax, an open-source software for electromagnetic simulations, to jointly generate models with geometrically random fragments, which are forward models with minimum human intervention. These easily prepared and simulated models can enrich the model training datasets which can be further utilized in machine learning for GPR retrieval algorithms.
Compact RF transmission unit for nuclear quadrupole resonance spectroscopy
Authors: Kijamnajsuk P , Sibsiri K , Luengviriya C 
Published: December 2023

Abstract
Nuclear quadrupole resonance (NQR) spectroscopy is a non-destructive measurement technique that enables to detect materials of interest very accurately. This technique works by sending high power electromagnetic waves with radio frequency (RF) very specific to the molecular structure to excite the sample and detecting the respond signal with the same frequency. Portable NQR detectors can be potentially developed to detect explosives with non-metallic containers in the fields. However, commercial RF high power amplifiers are large and heavy, so they are not practical for field usage. In this work, we demonstrate a compact NQR transmission part which composed of an active input signal conditioner, a class-D amplifier, and an NQR coil. The active input signal conditioner circuit will convert a single rf pulse with low amplitude (<0.5Vp) to two pulses (with 180° phase difference) and they are subsequently amplified to TTL level. In the class-D amplifier, the pre-amplified pulses are further amplified by a high voltage gate driver IC to 24V amplitude. These high voltage pulses are converted to a single 24V pulse by MOSFETs connected in a half bridge configuration. Finally, the amplified pulse is fed to the NQR coil, i.e., a low impedance LC series resonance circuit. The voltage across the coil at resonance frequency is about 1kVpp.
DeepHCC: Deep learning model for real-time count rate determination of particles in homemade cloud chamber
Authors: Srisukhawasu P , Silapasart A , Limjanon T , Madlee S , Samanrak C , Somnam T 
Published: December 2023

Abstract
Cosmic ray (CR) particles are high-energy particles originating from outer space that often reach the ground as alpha and proton particles, which can be studied using particle detectors. While investigating CR particles is a complex process, cloud chambers offer a simple means of detection. However, they cannot measure count rates directly. To overcome this limitation, we developed a deep learning model for real-time count rate determination of particles in a homemade cloud chamber (DeepHCC), implementing YOLOv5 pre-trained weights to use with our homemade cloud chamber. We trained DeepHCC using a total of 2,435 images from our homemade cloud chamber and other cloud chambers and found that YOLOv5m was the most suitable model for our task due to its fast and accurate detection capabilities. DeepHCC performed well in fundamental evaluations, with an overall area under the precision-recall curve of 0.8886 and an F1 score of 0.8624 while operating at 61 frames per second. We also experimented on count rate determination compared to human performance, which yielded an overall accuracy of 80.15%. Overall, DeepHCC could be used with our homemade cloud chambers to create particle detector kits with comparable performance. However, our model could not detect muons and accurately identify electrons due to an imbalanced dataset used in the training process. These reveal opportunities for future research to explore other techniques to address this problem. We also plan to compare our results with other detectors to verify reliability. The results demonstrate that our work could also be an innovative prototype for an engaging educational physics instrument.