bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Multi-dimensional and High-resolution Stereo Detection Technology of Urban Solid Waste Integrated With Open Space
Authors: Liu Wenqiang , Xu Xinxue , Li Chao , Wang Jiaqi , Ma Wei , Wang Junbao 
Published: December 2023

Abstract
The evolution law, waste type, volume and spatial distribution characteristics of a municipal solid waste landfill were studied by using multi-disciplinary comprehensive technologies such as surveying and mapping, geophysics, geological mapping, engineering drilling and three-dimensional modeling. Through the analysis of historical remote sensing and three-dimensional tilt photography data, the evolution law of solid waste is clarified, and the investigation scheme is formulated. With the help of GOCAD three-dimensional modeling software, the high-precision engineering borehole vertical layered positioning and high-resolution horizontal geophysical horizon tracking are organically combined to realize the high-precision and high-resolution intelligent analysis of spatial distribution and volume calculation of solid waste, The three-dimensional exploration system of eco-environmental geological survey is constructed.
A Case Study on Detection of Cavity in Urban Roads Using 3D Ground Penetrating Radar
Authors: Zhou Zhijun , Cui Yongxing 
Published: December 2023

Abstract
In recent years, urban roads collapse accidents often occur, which seriously threatens the safety of people’s lives and property and the order of urban operation, and has a huge social impact. Therefore, it is very necessary to carry out road cavity detection and eliminate the hidden dangers of road operation safety. Three dimensional ground penetrating radar (GPR) technology is a new technology developed in recent years. At present, it has been widely used in road detection. This paper briefly introduces the process of urban roads detection using 3D GPR, and introduces an example of cavity detection. The results show that the effect of 3D GPR in road cavity detection is remarkable, which is worth popularizing.
Near-Surface Radial Anisotropy Tomography of Geothermal Reservoir Using Dense Seismic Nodal Array
Authors: Guan Bo , Xia Jianghai , Liu Ya , Zhang Hao , Zhou Changwei 
Published: December 2023

Abstract
The development and utilization of geothermal energy has become an indispensable part in the process of urbanization in China. Near-surface tomographic imaging based on ambient noise is increasingly widely used in geothermal exploration. To construct the subsurface structure of Shear-wave velocity in Andi town, Zhejiang Province, recordings of ambient noise data of 4.7 days were recorded by a dense seismic array composed of 192 stations with an aperture of about 5 km. Analysis of array beamforming in high-frequency bands indicated the approximately uniformly distributed of noise sources. We applied seismic interferometry to retrieve both Rayleigh and Love waves. We used two-station analysis to obtain dispersion images and the corresponding high-quality dispersion curves were extracted by automated codes. 3-D tomographic results down to a depth of 1.6 km of S-wave velocity structure and radial anisotropy parameters were obtained using direct surface wave radial anisotropy tomography method. The anomalies of S-wave velocity and radial anisotropy parameters were combined to infer the storage of geothermal reservoir in Andi town. Furthermore, the accuracy of our results was verified by the borehole data.
Calculation of Noise Cross-Correlation Functions Between Different Types of Sensors without Removing Instrument Responses
Authors: Liu Ya , Xia Jianghai , Zhang Hao , Guan Bo , Xi Chaoqiang 
Published: December 2023

Abstract
The noise cross-correlation function between two stations provides an efficient way for understanding the subsurface structure. Removing instrument response is a necessary step for the calculation of the noise cross-correlation function (NCF) between different types of sensors, which requires the knowledge of the instrument response. However, the instrument response files may be missing or not provided, restricting us to only the calculation of NCF between the same type of instruments. We propose to calculate the NCF between different sensors deployed at the same site and obtain the phase differences between different instrument responses. The phase differences can then be removed resulting in the unbiased NCF between different sensors deployed at different locations. The effectiveness of our method is verified through a real data example.
Rayleigh Wave Dispersion and Inversion for Shallow Surface with a High-velocity Rigid Pavement
Authors: Zhang Jinyun , Zhang Zhifu , Zhang Yinjie , Lu Xuhan , Jiang Xianhong , Li Peng , Li Kang 
Published: December 2023

Abstract
We investigate the rigid pavement influence on phase velocity dispersion of the Rayleigh wave by using numerical modeling. An overestimation of Rayleigh wave phase velocity is observed more than 10% deviation caused by high-velocity rigid pavement. Based on the generalized reflection and transmission (R/T) coefficient method, the linear eigenvalue problem is solved for vertically heterogeneous media with a rigid pavement structure. The secular function demonstrates the multi-valuedness which is different phase velocity at the same frequency for the single Rayleigh mode. To address mode misidentification problem, we adopt a new misfit function for multimodal dispersion inversion. A Markov chain Monte Carlo (MCMC) sampling algorithm is used to solve dispersion inversion. The synthetic test shows that Rayleigh wave dispersion inversion incorporating a pavement layer can improve estimation accuracy of shear wave velocity.
Detection of Low-Velocity Layer Using Passive Surface-Wave Data and Deep Learning: An Application in The Urban Area of Hangzhou, China
Authors: Chen Xinhua , Xing Huaixue , Pang Jingyin , Zhou Changjiang , Xia Jianghai 
Published: December 2023

Abstract
Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) can release a tremendous workload brought by dense seismic arrays. We presented a case study of shear-wave velocity (Vs) structure imaging in the Hangzhou urban area (eastern China) using DL inversion. Noise data were recorded by dense linear arrays with approximately 5 m spacing deployed along two crossing roads for investigating the top 80 m of the subsurface. Phase-velocity dispersion curves are extracted from virtual shot gathers using multichannel analysis of surface waves. We divided the area where the low-velocity layer (LVL) may exist into three layers with a thickness of 5 m. We gave the four layers weak constraints to generate training dataset and adopted a convolutional neural network to directly invert fundamental-mode Rayleigh-wave phase velocity for 1D Vs models. To improve the accuracy, we further applied the sensitivities to weight the loss function in DL inversion. The obtained pseudo-2D Vs profiles correspond to the velocities estimated from logging data and previous survey. The well-trained neural network successfully identified that the LVL is located at 50-60 m deep. And this network was also achieved accurately the inversion of a dense seismic network nearby. The results of this survey demonstrate the accuracy and efficiency of delineating near-surface structures from traffic-induced noise using the DL technique, which has great potential for monitoring subsurface changes in urban areas.
Envelope Derivative Operator-based Instantaneous Attributes for Locating Road Cavities
Authors: Hu Xin , Zhang Xuebing , Qiu Zheli , Qin Yuxiang , Song Zhengchun 
Published: December 2023

Abstract
Ground penetrating radar, as an important means of near-surface detection, is being used more and more extensively in road detection. The instantaneous centroid frequency of ground penetrating radar has good directivity for road cavities, and the research of this group further improves the instantaneous centroid frequency for ground-penetrating radar road detection. In this paper, we will model and simulate ground penetrating radar detection to verify the effectiveness of the improved instantaneous pointing frequency.
Multi-parameter Inversion of Tunnels Based on Deep Learning Constraints
Authors: Jiang Peng , Tang Yuting , Liu Benchao 
Published: December 2023

Abstract
When tunnels traverse unfavorable geological formations, they often encounter the risk of sudden water-related incidents, posing a severe threat to the safety of construction workers and property. Consequently, there is an urgent demand for conducting research on the early detection of potential water-related disasters ahead of tunnel excavation. Among the various advanced exploration techniques, the DC resistivity method has proven to be more cost-effective and highly responsive to water-bearing structures, making it a prevalent choice for addressing sudden water hazards in real tunnel projects. This study introduces an innovative electrical approach underpinned by deep learning constraints, fusing resistivity and chargeability to pinpoint the location and size of underground water bodies. More specifically, we’ve designed a learning model rooted in the traditional U-Net convolutional network architecture, featuring a single encoder for feature extraction and a dual decoder for output. When compared to conventional linear inversion techniques and standalone parameter-based deep learning inversion methods, our proposed joint inversion approach exhibits superior performance.
Transdimensional Bayesian Inversion and Uncertainty Estimation of DC Resistivity Data for Undercover Cavern Characterization
Authors: Liu Yajun , Peng Ronghua , Guo Peng , Yu Gang , Liu Lihuang 
Published: December 2023

Abstract
The presence of subsurface karst caverns could pose significant threat to highway construction safety, and the knowledge about the locations and volumes of the caverns is crucial for evaluating potentially hazardous areas. In this work, we present a transdimensional Bayesian approach for characterizing undercover caverns by inverting direct current resistivity data., This approach provides a robust measure of uncertainty associated with the resulting resistivity model. We evaluate the effectiveness of the algorithm on a synthetic 2D resistivity model. The results demonstrate that quantitative uncertainty information on the derived model parameters is vital for reliable data interpretation and facilitates decision making.
Multi-station Magnetotelluric Data Processing Based on Singular Value Decompositionl
Authors: Yin Zihui , Guo Rongwen , Chen Hang 
Published: December 2023

Abstract
Singular value decomposition is an efficient matrix analysis method. This paper intends to use SVD to process magnetotelluric data from multiple stations. The data of multiple magnetotellus observation stations can make the main reactance of magnetotellus obtained by data processing more robust, suppress the interference signal, improve the data quality, and effectively increase the signal strength. The obtained results are more in line with the actual situation, and the decomposition by singular value method can greatly improve the efficiency of electromagnetic signal processing. Better access to underground information, geological resource exploration and deep earth research.
Numerical Simulation of 2D Strong Magnetic Field in Space-Wavenumber Mixed Domain
Authors: Ran Yingqiang , Dai Shikun , Chen Qingrui , Zhang Ying , Ling Jiaxuan 
Published: December 2023

Abstract
Strong magnetic body has self-demagnetization effect, which is not conducive to processing and interpretation of magnetic measurement data. A 2D numerical simulation method in the spatial-wavenumber mixed domain is proposed to calculate magnetic anomalies of 2D strong magnetic objects. In this method, the partial differential equation satisfying the magnetic potential of 2D strong magnetic body is transformed into independent ordinary differential equation between different wave numbers by 1D FFT, which greatly reduces the computation and storage requirements. The compact operator is used to solve the strong magnetic field by iterative method. The algorithm provides a new efficient and high-precision method for the forward modeling of the 2D magnetic anomalies.
A Case Study of Synchrosqueezing Wavelet Transform to Conventional Logging
Authors: Huang Zhaohui , Zang Yongqian , Lai Fuqiang , Jiang Guoqiang , Liu Yuejiao , Liu Yuanqi , Ou Fahui 
Published: December 2023

Abstract
In this paper, the synchronous squeezing wavelet transform (SSWT) is used to process the conventional logging curve, and the spectral characteristics of logging signal are obtained. The results show that the spectral characteristics of SSWT logging in tight sandstone reservoir are more obvious than that in depth domain, and have different characteristics for different reservoir fluid properties, which is helpful to identify the fluid properties of complex reservoirs.
Experimental Study on Influencing Factors of Soil Complex Resistivity
Authors: Han Yalu , Wang Yanlong , Tan Chun , Du Lizhi 
Published: December 2023

Abstract
Complex resistivity is one of the important parameters in electrical prospecting. The application of complex resistivity in oil and gas resource evaluation, engineering investigation and contaminated site evaluation can avoid the influence of low contrast on detection accuracy, and has a good application prospect. In order to improve the study of the influence of soil physical properties on complex resistivity and promote the wider engineering application of complex resistivity exploration. In this paper, the effects of dry density and water content on complex resistivity were studied by laboratory tests, and the following laws were obtained: under the same frequency, the lower the water content of soil sample, the higher the resistivity; The higher the degree of compaction, the lower the resistivity.
Experimental Study on the Performance of Steel Electrodes for Long-term Monitoring
Authors: Feng Shaoting , Yang Dikun 
Published: December 2023

Abstract
Long-term monitoring of electromagnetic (EM) fields requires dense arrays of electrode buried underground for a long time. Steel electrodes are cheap and easy to deploy but are well known to be prone to the noise due to the electrochemical reaction on the metal-fluid interface. In this experiment, we bury an array of steel and non-polarizable electrodes in a realistic monitoring setting, and investigate the performance of steel electrodes with the non-polarizable electrodes as reference. The pilot experiment of 8 days has found significant drift in the data from the steel electrodes, but a detrending process that removes the long-period signals can make the steel and non-polarizable electrode data more consistent at the time scale of a few minutes. The results show that the steel electrodes can have performance comparable with that of the non-polarizable electrodes if signals at relatively short periods are concerned. This study offers inspiration of collecting electrical field data by dense steel electrode arrays for a variety of monitoring applications.
Compression and Reconstruction of Magnetotelluric Data Based on Convolutional Neural Network
Authors: Guo Tianyu , Liu Jianxin , Guo Zhenwei 
Published: December 2023

Abstract
Magnetotelluric method is one of the important geophysical methods, and its signal acquisition requires more stacking times and longer stacking time. With the development of instruments, the acquisition time becomes longer and the amount of data becomes larger, which brings new challenges to data storage and transmission. Aiming at the above problems, a compression and reconstruction technology of magnetotelluric time series based on convolutional neural network is proposed. This paper introduces two convolutional autoencoders based on convolutional neural networks, which can effectively compress data space, improve transmission efficiency, and have high data reconstruction accuracy. Using the measured magnetotelluric time series, two autoencoder models based on convolutional networks are verified in this paper, which proves the feasibility of convolutional autoencoders in magnetotelluric data compression; the results show that model 2 can better reproduce It constructs the magnetotelluric time series, and has high training efficiency and good generalization ability.
Magnetotelluric Time Series Denoising Using Encoder-Decoder Consisted of LSTM Cells
Authors: Wang Sihao , He Lanfang , Wang Xuben , Li Liang 
Published: December 2023

Abstract
Electromagnetic signals in geophysics are frequently disturbed by various interference in field data acquisition. Denoising for passive electromagnetic methods such as magnetotelluric (MT) or audio magnetotelluric (AMT) data is significant to improve data quality and finally imaging to the geoelectrical structure. Conventionally, most denoising methods are employed in frequency domain and few of them are applied in time domain. However, a great number of irregular noise in the electromagnetic time series prove difficulty to be removed. We propose a denoising method, using Encoder-Decoder consisted of Long Short-Term Memory cells (ED-LSTM), to reduce the effect of the step noise and the random-impulsive noise. Supervised learning and transductive learning are used for the denoising of the step noise and the random-impulsive noise, respectively. Our results indicate that the step and random-impulsive noise could be successfully removed from the raw time series. The result indicate that ED-LSTM could potentially to be wildly used in the electromagnetic time series denoising and then improve data quality.
Research and Application of Deep Learning Selection Method for Magnetotelluric Power Spectrum Based on Rhoplus Correction
Authors: Xi Biao , Xie Xingbing , Zhou Lei 
Published: December 2023

Abstract
Magnetotelluric (MT) is an electromagnetic exploration method using natural field sources, which are unstable, irregular, and vulnerable to interference from electromagnetic signals. The total power spectrum of the magnetotelluric signal is superimposed by a sub-power spectrum of segmented time series. To reduce the influence of electromagnetic interference, the pre-processing of magnetotelluric data will delete the severely interfered sub-power spectrum. The selection of power spectrum often requires human-computer interaction, which is time-consuming and laborious, heavily dependent on personal experience, and has great randomness. This article innovation is put forward based on the classification of Rhoplus correction and deep learning automatic power spectrum method, first of all, the MT power spectrum impedance estimation results are Rhoplus one dimensional inversion and obtain fitting, using neural network learning fitting differential characteristics and classification, and then for the power spectrum of MT again after low-quality quantum power spectrum to eliminate overlay, Impedance estimation is performed, and the above process is repeated to find the optimal power spectrum combination. Through the comparison and verification of measured MT data, the automatic power spectrum selection method based on Rhoplus correction and deep learning classification achieve similar results to manual editing.
Automatic Recognition Method of Low SNR Regions Based on U-net
Authors: Sun Luping 
Published: December 2023

Abstract
The SNR (signal-to-noise ratio, SNR) attribute of seismic data is often used to evaluate the processing effect and help finding special geological bodies. Most of the SNR extraction methods need manual analysis to determine the location and time window, which has low precision and long consumption. In addition, the existing methods cannot accurately identify the boundary of low SNR region. This paper presents an automatic recognition method of low SNR regions of seismic data, which uses U-net (U-shaped fully convolutional network) to detect the boundaries of low SNR regions. The core of the method is to convert the recognition of SNR distribution into an image segmentation problem. We gives the specific process, takes the theoretical simulation data with different SNR as samples for network training, and uses the well trained model to segment seismic data with different SNR. The practical application shows the method has strong generalization ability, significantly improves the accuracy and efficiency of SNR recognition, and greatly reduces the workload of manual analysis.
A Shear Wave Prediction Based on Levenberg-Maequart Algorithm
Authors: Zhang Pengfei , Cui Yi-An , Zhang Shihui 
Published: December 2023

Abstract
Shear wave velocity plays an important role in geophysics. Usually, the compressional velocity information is contained in the log data, but the shear velocity information is missing due to the cost and imperfect technology. Therefore, accurate prediction of shear velocity is a necessary step in seismic inversion. In order to solve this problem, we propose a shear velocity prediction method based on Levenberg-Marquardt algorithm. It grows from the Elman neural network algorithm, which establishes a nonlinear mapping relationship between shear velocity and other log curves by optimizing the log curve weights, and achieves a reasonable prediction of shear velocity.
Experiment and Application of A Aparker Source in Seismic Exploration in Coastal Tidal Flats
Authors: Li Guangcai , Wang Xingyu , Wang Tong , Wang Kai 
Published: December 2023

Abstract
We investigate a field experiment in coastal tidal flats in Jiangsu Province using a sparker source, to test the seismic data acquisition system and detect the shallow layers in the real scenarios. We also compare the geophones with 60Hz and 10Hz natural frequency in shot gather as well as in stacked profiles. We process the different data separately, and then process them together in a wide-line mode. As predicted, the fold is doubled showing a higher signal to noise ratio. Through the routine multi-domain integrated noise removal and effective data processing, the signal-to-noise ratio and resolution of the seismic data get largely improved, and thus we successfully obtain the high-quality seismic profiles. The practical experiment results are of importance for shallow geological and environmental detections, as well as for geo-hazard prevention study in the related areas.
Noise Processing of Ocean Absolute Gravity Data Based on Savitzky-golay Filtering Algorithm
Authors: Yuan Peng , Qiao Zhongkun , Zhu Dong , Zhao Yingpeng , Zhou Yin , Xu Han , Wu Bin , Lin Qiang 
Published: December 2023

Abstract
High-precision data on absolute gravity have a wide range of applications, including basic science, resource exploration, national defense infrastructure, seismic studies, and inertial navigation, among others. The atomic gravimeter, known for its high sampling rate, accuracy and long-term stability, is particularly well suited for high-precision measurements of absolute gravity in oceanic and deep-sea environments. However, the challenging conditions found in the ocean introduce significant noise into the gravity data obtained from atomic gravimeters. This noise adversely affects the accuracy of gravity measurements, which in turn affects the interpretation of geological structures. Consequently, the analysis and processing of noise in atomic gravimeter data has become a focal point in the research of high-precision absolute oceanic gravity dynamic measurements. In this study, we conducted absolute gravity measurement experiments using the shipboard atomic absolute gravity measurement system developed at Zhejiang University of Technology. To filter the collected gravity data, we used the Savitzky-Golay (SG) filtering algorithm. This algorithm not only preserves essential measurement information, but also provides superior accuracy compared to conventional filtering methods.