Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference backgro...Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.展开更多
Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
The geometric characteristics of fractures within a rock mass can be inferred by the data sampling from boreholes or exposed surfaces.Recently,the universal elliptical disc(UED)model was developed to represent natural...The geometric characteristics of fractures within a rock mass can be inferred by the data sampling from boreholes or exposed surfaces.Recently,the universal elliptical disc(UED)model was developed to represent natural fractures,where the fracture is assumed to be an elliptical disc and the fracture orientation,rotation angle,length of the long axis and ratio of short-long axis lengths are considered as variables.This paper aims to estimate the fracture size-and azimuth-related parameters in the UED model based on the trace information from sampling windows.The stereological relationship between the trace length,size-and azimuth-related parameters of the UED model was established,and the formulae of the mean value and standard deviation of trace length were proposed.The proposed formulae were validated via the Monte Carlo simulations with less than 5%of error rate between the calculated and true values.With respect to the estimation of the size-and azimuth-related parameters using the trace length,an optimization method was developed based on the pre-assumed size and azimuth distribution forms.A hypothetical case study was designed to illustrate and verify the parameter estimation method,where three combinations of the sampling windows were used to estimate the parameters,and the results showed that the estimated values could agree well with the true values.Furthermore,a hypothetical three-dimensional(3D)elliptical fracture network was constructed,and the circular disc,non-UED and UED models were used to represent it.The simulated trace information from different models was compared,and the results clearly illustrated the superiority of the proposed UED model over the existing circular disc and non-UED models。展开更多
基于模块化多电平换流器的高压柔性直流输电系统(modular multilevel converter-based high voltage direct current,MMC-HVDC)常采用双极接线方式以提高系统功率输送能力和可靠性。然而目前对于风电场经柔直外送系统的稳定性研究集中...基于模块化多电平换流器的高压柔性直流输电系统(modular multilevel converter-based high voltage direct current,MMC-HVDC)常采用双极接线方式以提高系统功率输送能力和可靠性。然而目前对于风电场经柔直外送系统的稳定性研究集中于单极接线方式,孤岛直驱风电场与采用不同双极协调控制的双极MMC-HVDC互联系统小信号稳定性问题还有待进一步探究。该文首先考虑频率耦合特性、参考系初相位和直流侧耦合特性的影响,分别建立了采用双U/f下垂控制和定U/f-P/Q控制的双极MMC-HVDC系统交流侧等效SISO阻抗模型,并详细分析了金属回线阻抗和双极间功率均分度对交流阻抗特性的影响。接着对比研究了两种协调控制中共有控制环路和特有控制环路对交流侧负电阻特性及互联系统稳定性的影响规律。最后,孤岛直驱风电场经两种双极协调控制下双极MMC-HVDC外送系统Matlab/Simulink时域仿真结果和硬件在环半实物实时仿真实验结果验证了所提出的小信号阻抗模型的精确性和稳定性分析结论的有效性。展开更多
文摘Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
基金funded by National Natural Science Foundation of China(Grant No.41972264)Zhejiang Provincial Natural Science Foundation of China(Grant No.LR22E080002)the Observation and Research Station of Geohazards in Zhejiang,Ministry of Natural Resources,China(Grant No.ZJDZGCZ-2021).
文摘The geometric characteristics of fractures within a rock mass can be inferred by the data sampling from boreholes or exposed surfaces.Recently,the universal elliptical disc(UED)model was developed to represent natural fractures,where the fracture is assumed to be an elliptical disc and the fracture orientation,rotation angle,length of the long axis and ratio of short-long axis lengths are considered as variables.This paper aims to estimate the fracture size-and azimuth-related parameters in the UED model based on the trace information from sampling windows.The stereological relationship between the trace length,size-and azimuth-related parameters of the UED model was established,and the formulae of the mean value and standard deviation of trace length were proposed.The proposed formulae were validated via the Monte Carlo simulations with less than 5%of error rate between the calculated and true values.With respect to the estimation of the size-and azimuth-related parameters using the trace length,an optimization method was developed based on the pre-assumed size and azimuth distribution forms.A hypothetical case study was designed to illustrate and verify the parameter estimation method,where three combinations of the sampling windows were used to estimate the parameters,and the results showed that the estimated values could agree well with the true values.Furthermore,a hypothetical three-dimensional(3D)elliptical fracture network was constructed,and the circular disc,non-UED and UED models were used to represent it.The simulated trace information from different models was compared,and the results clearly illustrated the superiority of the proposed UED model over the existing circular disc and non-UED models。
基金supported by the National Natural Science Foundation of China [grant number 42276008]the Laoshan Laboratory[grant number LSKJ202202403-2]+2 种基金supported by the National Natural Science Foundation of China [grant number 42030410]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB40000000]the Startup Foundation for Introducing Talent of NUIST
文摘基于模块化多电平换流器的高压柔性直流输电系统(modular multilevel converter-based high voltage direct current,MMC-HVDC)常采用双极接线方式以提高系统功率输送能力和可靠性。然而目前对于风电场经柔直外送系统的稳定性研究集中于单极接线方式,孤岛直驱风电场与采用不同双极协调控制的双极MMC-HVDC互联系统小信号稳定性问题还有待进一步探究。该文首先考虑频率耦合特性、参考系初相位和直流侧耦合特性的影响,分别建立了采用双U/f下垂控制和定U/f-P/Q控制的双极MMC-HVDC系统交流侧等效SISO阻抗模型,并详细分析了金属回线阻抗和双极间功率均分度对交流阻抗特性的影响。接着对比研究了两种协调控制中共有控制环路和特有控制环路对交流侧负电阻特性及互联系统稳定性的影响规律。最后,孤岛直驱风电场经两种双极协调控制下双极MMC-HVDC外送系统Matlab/Simulink时域仿真结果和硬件在环半实物实时仿真实验结果验证了所提出的小信号阻抗模型的精确性和稳定性分析结论的有效性。
