Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures....Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.展开更多
In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the genera...In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the generating matrix G. Then we present a new soft-decision decoding based on HDGA and the Chase algorithm (SDGA). The performance of some binary and non-binary Linear Block Codes are given for HDGA and SDGA over Gaussian and Rayleigh channels. The performances show that the HDGA decoder has the same performances as the Berlekamp-Massey Algorithm (BMA) in various transmission channels. On the other hand, the performances of SDGA are equivalent to soft-decision decoding using Chase algorithm and BMA (Chase-BMA). The complexity of decoders proposed is also discussed and compared to those of other decoders.展开更多
The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method wi...The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method with bias correction is proposed.This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function(LSF)and the signed distance function.Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel.Thirdly introducing the offset correction term and fully using the local clustering property of image intensity,the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation.Finally,the fractional distance regularization,offset correction,and external energy constraints are combined,and the energy optimization segmentation method for noisy image is established by level set.Experimental results show that the proposed method can accurately segment the image,and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms.展开更多
用于航天器轨道预报的热层密度模型普遍存在30%左右的误差,影响LEO卫星的精密轨道确定和载荷控制。基于低轨航天器平运动变化与大气密度的关系,使用GRACE(gravity recovery and climate experiment)卫星TLE数据反演2003、2007年沿轨大...用于航天器轨道预报的热层密度模型普遍存在30%左右的误差,影响LEO卫星的精密轨道确定和载荷控制。基于低轨航天器平运动变化与大气密度的关系,使用GRACE(gravity recovery and climate experiment)卫星TLE数据反演2003、2007年沿轨大气密度,通过比较反演值、模型值和实测值的关系分析误差产生原因,使用对数正态分布拟合密度比值。通过分析太阳辐射、地磁指数对大气密度变化的影响,提出一种基于空间环境指数的热层大气密度模型校正与预报方式。使用该方法对2003、2004、2007、2008年的MSIS86模型计算密度进行修正,将模型平均相对误差从33.33%~59.62%降低到11.55%~15.13%,太阳活动低年改进量是高年的1.5~2倍。对2009年经验模型结果进行预报校正,将预报误差降低36.49%,提高了模型精度。展开更多
基金This study work is supported by the Directly Managed Scientifi c Research Project of Huainan Mining(Group)Co.Ltd.(No.HNKYJTJS(2018)181),the Major Project of Shaanxi Coal and Chemical Industry Group Co.Ltd.(No.2018SMHKJ-A-J-03),China Energy Investment Corporation 2030 Pilot Project(No.GJNY2030XDXM-19-03.2),State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology(Beijing).I also would like to thank the editorial department and the review experts for their valuable comments and suggestions,and thank the Compagnie Générale de Géophysique(CGG)for the Jason software support.
文摘Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.
文摘In this paper, we describe a hard-decision decoding technique based on Genetic Algorithms (HDGA), which is applicable to the general case of error correcting codes where the only known structure is given by the generating matrix G. Then we present a new soft-decision decoding based on HDGA and the Chase algorithm (SDGA). The performance of some binary and non-binary Linear Block Codes are given for HDGA and SDGA over Gaussian and Rayleigh channels. The performances show that the HDGA decoder has the same performances as the Berlekamp-Massey Algorithm (BMA) in various transmission channels. On the other hand, the performances of SDGA are equivalent to soft-decision decoding using Chase algorithm and BMA (Chase-BMA). The complexity of decoders proposed is also discussed and compared to those of other decoders.
基金This work was supported by the National Natural Science Foundation of China(62071378).
文摘The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method with bias correction is proposed.This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function(LSF)and the signed distance function.Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel.Thirdly introducing the offset correction term and fully using the local clustering property of image intensity,the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation.Finally,the fractional distance regularization,offset correction,and external energy constraints are combined,and the energy optimization segmentation method for noisy image is established by level set.Experimental results show that the proposed method can accurately segment the image,and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms.
文摘用于航天器轨道预报的热层密度模型普遍存在30%左右的误差,影响LEO卫星的精密轨道确定和载荷控制。基于低轨航天器平运动变化与大气密度的关系,使用GRACE(gravity recovery and climate experiment)卫星TLE数据反演2003、2007年沿轨大气密度,通过比较反演值、模型值和实测值的关系分析误差产生原因,使用对数正态分布拟合密度比值。通过分析太阳辐射、地磁指数对大气密度变化的影响,提出一种基于空间环境指数的热层大气密度模型校正与预报方式。使用该方法对2003、2004、2007、2008年的MSIS86模型计算密度进行修正,将模型平均相对误差从33.33%~59.62%降低到11.55%~15.13%,太阳活动低年改进量是高年的1.5~2倍。对2009年经验模型结果进行预报校正,将预报误差降低36.49%,提高了模型精度。
