检索结果-内蒙古大学图书馆

Deformation Mechanism in Central China Revealed by Regional Earthquake Tomography

Seismological Research Letters 2025年第3期96卷 1561-1572页

作者： Hua, Yujin Zhang, Shuangxi Liu, Yu Liu, Li Wu, Tengfei Wei, Yu Li, Bin Department of Earth Sciences and Engineering Taiyuan University of Technology Taiyuan China Department of Geophysics School of Geodesy and Geomatics Wuhan University Wuhan China School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang China College of Civil Engineering Taiyuan University of Technology Taiyuan China School of Civil Engineering and Architecture Hubei Polytechnic University Huangshi China China Huadian Corporation Ltd. Beijing China

The central China region (CCN) has experienced significant deformation and complex structural arrangements. We conducted research on the deformation pattern of the CCN through the 3D velocity structure of the crust and uppermost mantle obtained via body-wave tomography. This velocity structure strongly correlates with the region’s topographic features. The relationship between velocity distributions and topography indicates that compression in the east–west direction, which can lead to crustal and mantle shortening, may play a crucial role in shaping topography. The various structures between the Qinling mountain range in the west and the Tongbai–Dabie mountain range in the east highlight distinct geological processes occurring along the Qinling–Dabie orogenic belt. In addition, we propose that reservoir water has infiltrated the crystalline basement rock layer up to 15–20 km beneath the Three Gorges Reservoir. Furthermore, the Three Gorges Dam serves as a solid foundation to prevent reservoir water from seeping downstream underground. © Seismological Society of America.

关键词： Crystalline rocks

来源：评论

学校读者我要写书评

暂无评论

Fast resistivity imaging of transient electromagnetic using an extreme learning machine

引用

Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

来源：评论

学校读者我要写书评

暂无评论

An improved ICEEMDAN-MPA-GRU model for GNSS height time series prediction with weighted quality evaluation index

引用

GPS Solutions 2025年第3期29卷 1-19页

作者： Zhou, Yu He, Xiaoxing Montillet, Jean-Philippe Wang, Shengdao Hu, Shunqiang Sun, Xiwen Huang, Jiahui Ma, Xiaping Jiangxi University of Science and Technology 1958 Ke-jia Road Ganzhou341000 China Institute Dom Luiz University of Beira Interior Covilhã6201-001 Portugal Davos7260 Switzerland Division of Geodetic Science School of Earth Sciences The Ohio State University Columbus ColumbusOH43210 United States Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education Jiangxi Normal University Nanchang330022 China School of Geophysics and Measurement-control Technology East China University of Technology Nanchang330000 China College of Geomatics Xi’an University of Science and Technology Xi’an710054 China

High-precision GNSS height time series prediction can provide a critical reference for applications such as regional or global reference frame maintenance, crustal movements, structural safety monitoring, and geological hazards. An improved ICEEMDAN-MPA-GRU model has been developed to achieve high-precision prediction results. The Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is used to decompose the original time series, and subsequently the Gated Recurrent Unit (GRU) is employed for the forecast of the time series. Marine Predators Algorithm (MPA) is also included to optimize the parameters of the algorithms (e.g., number of neurons, learning rate, and iteration count). The ICEEMDAN-MPA-GRU model is applied to the daily height coordinate (Up component) of 14 GNSS stations in North American with time span of 20 years (2000.0–2020.0). A comprehensive evaluation was performed using the proposed Weighted Quality Evaluation index (WQE). The ICEEMDAN-MPA-GRU model achieved an average WQE of 0.463. Compared to the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), MPA-GRU, and ICEEMDAN-MPA-LSTM models, the ICEEMDAN-MPA-GRU model reduced the prediction WQE by an average of 63.77%, 58.47%, 54.20%, and 30.09%, respectively. Furthermore, the ICEEMDAN-MPA-GRU model demonstrated strong robustness in the presence of data gaps. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Health hazards

来源：评论

学校读者我要写书评

暂无评论

Inversion of airborne tipper and ground-based data for two-dimensional radio-magnetotelluric using the Gauss-Newton method

引用

Journal of Applied geophysics 2025年 239卷

作者： Yi, Ke Zhang, Zhiyong Zhou, Feng Li, Man Wan, Jianhua Huang, Lei Deng, Hongze School of Geophysics and Measurement-control Technology East China University of Technology Nanchang330013 China Institute of Geophysics and Geomatics China University of Geosciences Wuhan430074 China Key Laboratory of Airborne Geophysics and Remote Sensing Geology Ministry of Natural Resources Beijing100083 China CGNPC Uranium Resources Co. Ltd. Beijing100029 China

A two-dimensional inversion algorithm based on the Gauss-Newton optimization has been developed for both ground-based and airborne data of Radio-magnetotelluric (RMT). The Finite Element Method (FEM) has been used in the forward modelling of RMT based on the unstructured mesh. The ground-based responses of a synthetic model including apparent resistivity, phase and tipper have been inverted and the lateral resolution of tipper has been evaluated. Then the effect of observed altitude, topography, and noise level on the airborne RMT tipper response and inverted model has been evaluated. Finally, the simultaneous inversion of ground-based data and airborne tipper has been carried out. The synthetic examples indicate that the inversion incorporating tipper data can more distinctly identify lateral resistivity variation in geological structures. The available anomaly included in the airborne tipper caused by the target structure diminishes as observed altitude increases. And the effect of topography on airborne tipper cannot be ignored, which can be eliminated by inversion with topographic consideration. When adequate airborne tipper data can be acquired, a small number of ground-based impedance data is need to be measured for obtaining resistivity values. The combined strategy of dense airborne tipper with sparse ground-based impedance measurement makes the exploration more effective and keeps great reliability for electrical resistivity imaging. © 2025 Elsevier B.V.

关键词： Magnetotellurics

来源：评论

学校读者我要写书评

暂无评论

Correction: Hybrid GNSS time-series prediction method based on ensemble empirical mode decomposition with long short-term memory

引用

Discover Applied Sciences 2025年第2期7卷 1-2页

作者： Zhou, Yu He, Xiaoxing Wang, Shengdao Hu, Shunqiang Sun, Xiwen Huang, Jiahui School of Civil and Surveying and Mapping Engineering Jiangxi University of Science and Technology Ganzhou China Division of Geodetic Science School of Earth Sciences The Ohio State University Columbus USA School of Geography and Environment Jiangxi Normal University Nanchang China School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang China

来源：评论

学校读者我要写书评

暂无评论

Automatic location of surface-monitored microseismicity with deep learning

引用

Earthquake Research Advances 2025年第2期5卷

作者： Gan, Zhaolong Tian, Xiao Zhang, Xiong Dai, Mengxue Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province (East China University of Technology) Nanchang 330013 China School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang 330013 China

Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring. Traditional methods, such as diffraction stacking, are time-consuming and challenging for real-time monitoring. In this study, we propose an approach to locate microseismic events using a deep learning algorithm with surface data. A fully convolutional network is designed to predict source locations. The input data is the waveform of a microseismic event, and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x, y, and z dimensions. The theoretical dataset is generated to train the model, and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data. After applying the trained model to field data, the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method, making it promising for real-time microseismic monitoring. © 2024 China Earthquake Networks Center.

关键词： Deep learning Microseismic monitoring Source location

来源：评论

学校读者我要写书评

暂无评论

Design of student behavior characteristics analysis platform based on K-means clustering algorithm in local universities 3

Design of student behavior characteristics analysis platform...

引用

3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Xiao, Kun Duan, Zhongyi Li, Mingen Yao, Jinkui East China University of Technology School of Geophysics and Measurement-control Technology Nanchang China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Student behavior characteristic data can fully reflect the behavior law of college students, which is of great significance to the educational management and decision-making of local universities. With the gradual acceleration of information construction in colleges and universities, most colleges and universities have developed the campus information management system. Facing the massive campus management data, how to effectively mine valuable information from it has become an important content to improve the level of student information management. This paper uses big data processing and analysis technology to comprehensively analyze the current big data of local universities. Moreover, a distributed computing platform based on K-means clustering algorithm is constructed based on the behavioral characteristics of college students, and a prediction model between student behavior and talent quality cultivation is constructed. This research can guide teaching management, student management, employment guidance and other related work in local universities. © 2022 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

The self-adaptive DLMS denoising method for marine controlled-source data based on distance factor

引用

C e Ca 2017年第1期42卷 245-253页

作者： Li, Zelin Hu, Xiangyun Deng, Juzhi Wu, Xinmin Li, Hailin Huang, Shuhua Institute of Geophysics and Geomatics China University of Geosciences Wuhan430074 China School of Geophysics and Measurement-control Technology East China University of Technology Nanchang330013 China

Marine controlled-source electromagnetic method (MCSEM) is a frequency domain electromagnetic method based on the difference of resistivity between seabed reservoir and surrounding rock. Noises in the received signal pose strong interferences to the data post-processing and information interpretation of marine controlled source. This paper conducts an analysis of MCSEM's principle, its signal propagation mode and the characteristics of noises to be removed. Besides, the self-adaptive LMS filtering method is introduced to suppress the noise to better recover and reconstruct the processed signal. In the meantime, based on the traditional LMS algorithm and the characteristics of offset-varying MCSEM data, this paper devises a variable step size DLMS algorithm based on distance factor adjustment. Specifically, the existing algorithms are revised to strike a balance between the system convergence speed and steady-state error. Moreover, the DLMS algorithm is used to process theoretical models and measure data. The results show that the proposed method can effectively restore the characteristics and details of MVO curves and apparent resistivity curves. Thus, this paper provides an effective means of the subsequent marine electromagnetic data processing and data interpretation.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Gravity Minimum Support Regularization Inversion Combined with Prior Image Information 20

Three-Dimensional Gravity Minimum Support Regularization Inv...

引用

20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Yan, Wen Qiang Chen, Xiao Wang, JinFeng Zhao, Fei Yu School of Geophysics and Measurement-Control Technology East China University of Technology Nanchang China

ISBN: (纸本)9798350356328

In a geophysical inversion, forms of prior information are various. However, it is not easy to obtain prior lithologic or structural information. In order to integrate image information into the geophysical inversion, this paper takes the 3D gravity regularization inversion as an example, integrates prior image information into a minimum support stabilizer, and proposes a new stabilizer. Then, single-block and double-block model tests are designed and implemented. Compared with the result using the minimum support stabilizer, the result of using the new stabilizer can better restores the lithologic and structural distribution of underground anomaly. Besides, the resolution of the gravity method is improved. It can be concluded that the new stabilizer is suitable for 3D gravity inversion, providing a new idea for the integration of prior information in geophysical inversion. © 2024 IEEE.

关键词： Lithology

来源：评论

学校读者我要写书评

暂无评论

Study on conical source transient electromagnetic based on AWPSO algorithm 9

Study on conical source transient electromagnetic based on A...

引用

9th International Conference on Environmental and Engineering geophysics, ICEEG 2020

作者： Huang, Gang Yang, Haiyan School of Geophysics and Measurement and Control Technology East China University of Technology Nanchang China

The transient electromagnetic conical field source device can effectively reduce the inductance between the coils and improve the resolution of the small device to detect the shallow underground layer. However, the conventional inversion method requires an initial model and has the disadvantage of low inversion precision. Combined with the results of previous studies, this paper proposed an adaptive weighted particle swarm optimization (AWPSO) algorithm based on Sigmoid function through the analysis and research of PSO, which solved the problem of artificial initial model and low precision of inversion results. By calculating the test function, the algorithm can effectively improve the searching ability of particle swarm optimization algorithm. Several different typical geoelectric models are designed, and the algorithm is applied to data inversion of transient electromagnetic geoelectric model. Combined with the measured data of a hot spring practice area, the inversion calculation and interpretation of the sounding data were carried out, and the inversion calculation results were confirmed by the geological and hydrological data. The results show that the proposed particle swarm optimization algorithm has a high inversion fitting degree to the theoretical model and the measured data, achieves the separation of the apparent resistivity anomalies in the shallow subsurface, and improves the resolution and interpretation accuracy of the measured data in the conical field source transient electromagnetic method. © Published under licence by IOP Publishing Ltd.

关键词： Hot springs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：