The propagation of seismic waves in viscous media,such as the loess plateau and shallow gas regions,alters their amplitude,frequency,and phase due to absorption attenuation,resulting in reductions in the resolution an...The propagation of seismic waves in viscous media,such as the loess plateau and shallow gas regions,alters their amplitude,frequency,and phase due to absorption attenuation,resulting in reductions in the resolution and fidelity of seismic profiles and the inaccurate identification of subtle structure and lithology.Q modeling and Q migration techniques proposed in this paper are used to compensate for the energy and frequency attenuation of seismic waves,obtain high-quality depth imaging results,and further enhance structural imaging to address the aforementioned problem.First,various prior information is utilized to construct an initial Q model.Q tomography techniques are employed to further optimize the precision of the initial Q model and build a high-precision Q model.Subsequently,Q prestack depth migration technology is employed to compensate for absorption and attenuation in the three-dimensional space along the seismic wave propagation path and correct the travel times,realizing the purposes of amplitude compensation,frequency recovery,and phase correction,which can help improve the wave group characteristics while enhancing the resolution.Model data and practical application results demonstrate that high-precision Q modeling and Q migration techniques can substantially improve the imaging quality of underground structures and formations in the loess plateau region with extremely complex surface and near-surface conditions.The resolution and fidelity of seismic data,as well as the capability to identify reservoirs,can be improved using these techniques.展开更多
Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- a...Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.展开更多
Chaos game representation （CGR） is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to determine the coordinates of their ...Chaos game representation （CGR） is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to determine the coordinates of their positions in a continuous space. This distribution of positions has two features： one is unique, and the other is source sequence that can be recovered from the coordinates so that the distance between positions may serve as a measure of similarity between the corresponding sequences. A CGR-walk model is proposed based on CGR coordinates for the DNA sequences. The CGR coordinates are converted into a time series, and a long-memory ARFIMA （p, d, q） model, where ARFIMA stands for autoregressive fractionally integrated moving average, is introduced into the DNA sequence analysis. This model is applied to simulating real CGR-walk sequence data of ten genomic sequences. Remarkably long-range correlations are uncovered in the data, and the results from these models are reasonably fitted with those from the ARFIMA （p, d, q） model.展开更多
在工程中,大型设备和精密仪器在运行时,产生的振动通常会对周围环境造成影响;因此,研究抑制振动的隔振系统及控制方法具有重要意义。为了扩大隔振范围以隔振大型物体,设计了具有多个电磁隔振单元的并联电磁隔振系统,并提出了一种融合Q...在工程中,大型设备和精密仪器在运行时,产生的振动通常会对周围环境造成影响;因此,研究抑制振动的隔振系统及控制方法具有重要意义。为了扩大隔振范围以隔振大型物体,设计了具有多个电磁隔振单元的并联电磁隔振系统,并提出了一种融合Q学习的非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)方法实时调控该多隔振单元系统,以提高系统的隔振性能。基于电磁力、线圈电流和电磁铁间距三者的非线性关系建立了并联电磁隔振系统的动力学方程及状态方程,在此基础上设计了NMPC控制器。其中,利用Q学习方法确定了预测范围,从而避免计算量过大或预测模型不准确的问题;同时,Q学习方法能够优化NMPC方法的目标函数中的权重矩阵V和R。仿真和实验结果表明,在所提出的融合Q学习的NMPC方法控制下的多隔振单元并联系统在外界扰动下,振动幅度显著减小,系统平稳性大大提高。展开更多
A new chaos game representation of protein sequences based on the detailed hydrophobic-hydrophilic （HP） model has been proposed by Yu et al （Physica A 337（2004） 171）. A CGR-walk model is proposed based on the ne...A new chaos game representation of protein sequences based on the detailed hydrophobic-hydrophilic （HP） model has been proposed by Yu et al （Physica A 337（2004） 171）. A CGR-walk model is proposed based on the new CGR coordinates for the protein sequences from complete genomes in the present paper. The new CCR coordinates based on the detailed HP model are converted into a time series, and a long-memory ARFIMA（p, d, q） model is introduced into the protein sequence analysis. This model is applied to simulating real CCR-walk sequence data of twelve protein sequences. Remarkably long-range correlations are uncovered in the data and the results obtained from these models are reasonably consistent with those available from the ARFIMA（p, d, q） model.展开更多
基金supported by the China National Offshore Oil Corporation’s“14th Five-Year Plan”major scientific and technological project,“Key Technologies for Onshore Unconventional Natural Gas Exploration and Development”(KJGG2021-1000).
文摘The propagation of seismic waves in viscous media,such as the loess plateau and shallow gas regions,alters their amplitude,frequency,and phase due to absorption attenuation,resulting in reductions in the resolution and fidelity of seismic profiles and the inaccurate identification of subtle structure and lithology.Q modeling and Q migration techniques proposed in this paper are used to compensate for the energy and frequency attenuation of seismic waves,obtain high-quality depth imaging results,and further enhance structural imaging to address the aforementioned problem.First,various prior information is utilized to construct an initial Q model.Q tomography techniques are employed to further optimize the precision of the initial Q model and build a high-precision Q model.Subsequently,Q prestack depth migration technology is employed to compensate for absorption and attenuation in the three-dimensional space along the seismic wave propagation path and correct the travel times,realizing the purposes of amplitude compensation,frequency recovery,and phase correction,which can help improve the wave group characteristics while enhancing the resolution.Model data and practical application results demonstrate that high-precision Q modeling and Q migration techniques can substantially improve the imaging quality of underground structures and formations in the loess plateau region with extremely complex surface and near-surface conditions.The resolution and fidelity of seismic data,as well as the capability to identify reservoirs,can be improved using these techniques.
文摘Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.
基金Project supported by the National Natural Science Foundation of China (Grant No 60575038)the Natural Science Foundation of Jiangnan University,China (Grant No 20070365)
文摘Chaos game representation （CGR） is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to determine the coordinates of their positions in a continuous space. This distribution of positions has two features： one is unique, and the other is source sequence that can be recovered from the coordinates so that the distance between positions may serve as a measure of similarity between the corresponding sequences. A CGR-walk model is proposed based on CGR coordinates for the DNA sequences. The CGR coordinates are converted into a time series, and a long-memory ARFIMA （p, d, q） model, where ARFIMA stands for autoregressive fractionally integrated moving average, is introduced into the DNA sequence analysis. This model is applied to simulating real CGR-walk sequence data of ten genomic sequences. Remarkably long-range correlations are uncovered in the data, and the results from these models are reasonably fitted with those from the ARFIMA （p, d, q） model.
文摘在工程中,大型设备和精密仪器在运行时,产生的振动通常会对周围环境造成影响;因此,研究抑制振动的隔振系统及控制方法具有重要意义。为了扩大隔振范围以隔振大型物体,设计了具有多个电磁隔振单元的并联电磁隔振系统,并提出了一种融合Q学习的非线性模型预测控制(Nonlinear Model Predictive Control,NMPC)方法实时调控该多隔振单元系统,以提高系统的隔振性能。基于电磁力、线圈电流和电磁铁间距三者的非线性关系建立了并联电磁隔振系统的动力学方程及状态方程,在此基础上设计了NMPC控制器。其中,利用Q学习方法确定了预测范围,从而避免计算量过大或预测模型不准确的问题;同时,Q学习方法能够优化NMPC方法的目标函数中的权重矩阵V和R。仿真和实验结果表明,在所提出的融合Q学习的NMPC方法控制下的多隔振单元并联系统在外界扰动下,振动幅度显著减小,系统平稳性大大提高。
基金Project supported by the National Natural Science Foundation of China (Grant No 60575038)the Natural Science Foundation of Jiangnan University, China (Grant No 20070365)the Program for Innovative Research Team of Jiangnan University, China
文摘A new chaos game representation of protein sequences based on the detailed hydrophobic-hydrophilic （HP） model has been proposed by Yu et al （Physica A 337（2004） 171）. A CGR-walk model is proposed based on the new CGR coordinates for the protein sequences from complete genomes in the present paper. The new CCR coordinates based on the detailed HP model are converted into a time series, and a long-memory ARFIMA（p, d, q） model is introduced into the protein sequence analysis. This model is applied to simulating real CCR-walk sequence data of twelve protein sequences. Remarkably long-range correlations are uncovered in the data and the results obtained from these models are reasonably consistent with those available from the ARFIMA（p, d, q） model.
