Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density fu...Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.展开更多
This study has developed an improved subjective approach of classification in conjunction with Stepwise DFA analysis to discriminate Chinese sturgeon signals from other targets. The results showed that all together 25...This study has developed an improved subjective approach of classification in conjunction with Stepwise DFA analysis to discriminate Chinese sturgeon signals from other targets. The results showed that all together 25 Chinese sturgeon echo-signals were detected in the spawning ground of Gezhouba Dam during the last 3 years, and the identification accuracy reached 90.9%. In Stepwise DFA, 24 out of 67 variables were applied in discrimination and identification. PCA combined with DFA was then used to ensure the significance of the 24 variables and detailed the identification pattern. The results indicated that we can discriminate Chinese sturgeon from other fish species and noise using certain descriptors such as the behaviour variables, echo characteristics and acoustic cross-section characteristics. However, identification of Chinese sturgeon from sediments is more difficult and needs a total of 24 variables. This is due to the limited knowledge about the acoustic-scattering properties of the substrate regions. Based on identified Chinese sturgeon individuals, 18 individuals were distributed in the region between the site of Gezhouba Dam and Miaozui reach, with a surface area of about 3.4 km2. Seven individuals were distributed in the region between Miaozui and Yanshouba reach, with a surface area of about 13 km2.展开更多
Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is a...Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.展开更多
为进一步提高变压器机械故障智能诊断的准确性,文中基于变压器振动信号时间序列符号化的模式表征,提出了一种基于栈式自编码器的变压器机械故障诊断模型。首先对振动信号时间序列进行符号化模式表征和构建复杂网络,提取了基于度分布的...为进一步提高变压器机械故障智能诊断的准确性,文中基于变压器振动信号时间序列符号化的模式表征,提出了一种基于栈式自编码器的变压器机械故障诊断模型。首先对振动信号时间序列进行符号化模式表征和构建复杂网络,提取了基于度分布的变压器振动信号特征向量,据此构建了基于栈式自编码器(stacked auto encoder,SAE)的变压器机械故障诊断模型。对某10 k V干式变压器正常与典型机械故障下振动信号的分析结果表明,变压器振动信号时间序列的符号化模式表征及度分布能较好地表征其动力学特征,所构建的基于SAE变压器机械故障模型具有较高的识别准确率,可达95%,研究结果可为变压器的机械故障诊断提供新思路。展开更多
采用示踪技术探索了3 H JA的运输和分配规律及其受伤害胁迫的影响。外源 3 H JA能够在小麦幼苗体内向上和向下运输 ,局部灼伤其运输与分配都发生了改变。从小麦根系饲喂的3 H JA ,在植株内的分布量依序为根 >茎 >叶 ,时间较长 (4h...采用示踪技术探索了3 H JA的运输和分配规律及其受伤害胁迫的影响。外源 3 H JA能够在小麦幼苗体内向上和向下运输 ,局部灼伤其运输与分配都发生了改变。从小麦根系饲喂的3 H JA ,在植株内的分布量依序为根 >茎 >叶 ,时间较长 (4h)时分配于心叶的3 H JA大大增加。当叶片受到局部灼伤时3 H JA向地上部的输出量减少 ;但局部灼伤可加快由心叶饲喂的3 H JA的向下运输 ,改变3 H JA在小麦幼苗各部位的分配比率。心叶饲喂短时间 (5min)时 ,3 H JA主要积累在受到伤胁迫的展开叶 (第 2叶 )中。向展开叶 (第2叶 )饲喂的3 H JA向下运输的速率高于向上运输的速率。展开更多
基金supported by the National Major Science and Technology Project of China on Development of Big Oil-Gas Fields and Coalbed Methane (No. 2008ZX05010-002)
文摘Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.
基金Supported by the National Natural Science Foundation of China (Grant No. 30490234)Special Public Sector Research of the Ministry of Water Resources of China (Grant Nos. 200701029, 200701008)
文摘This study has developed an improved subjective approach of classification in conjunction with Stepwise DFA analysis to discriminate Chinese sturgeon signals from other targets. The results showed that all together 25 Chinese sturgeon echo-signals were detected in the spawning ground of Gezhouba Dam during the last 3 years, and the identification accuracy reached 90.9%. In Stepwise DFA, 24 out of 67 variables were applied in discrimination and identification. PCA combined with DFA was then used to ensure the significance of the 24 variables and detailed the identification pattern. The results indicated that we can discriminate Chinese sturgeon from other fish species and noise using certain descriptors such as the behaviour variables, echo characteristics and acoustic cross-section characteristics. However, identification of Chinese sturgeon from sediments is more difficult and needs a total of 24 variables. This is due to the limited knowledge about the acoustic-scattering properties of the substrate regions. Based on identified Chinese sturgeon individuals, 18 individuals were distributed in the region between the site of Gezhouba Dam and Miaozui reach, with a surface area of about 3.4 km2. Seven individuals were distributed in the region between Miaozui and Yanshouba reach, with a surface area of about 13 km2.
基金This work was supported by the National Natural Science Foundation of China (Grant Nos. 39980009,69790080) the 973 Project (Grant No. G1998030503) the Foundation for University Key Teacher by the Ministry of Education, China Sichuan Youth Researche
文摘Independent component analysis (ICA) is a newly developed promising technique in signal processing applications. The effective separation and discrimination of functional Magnetic Resonance Imaging (fMRI) signals is an area of active research and widespread interest. Therefore, the development of an ICA based fMRI data processing method is of obvious value both theoretically and in potential applications. In this paper, analyzed firstly is the drawback of the extant popular ICA-fMRI method where the adopted signal model assumes the independence of spatial distributions of the signals and noise. Then presented is a new fMRI signal model, which assumes the independence of temporal courses of signal and noise in a tiny spatial domain. Consequently we get a novel fMRI data processing method: Neighborhood independent component correlation algorithm. The effectiveness is elucidated through theoretical analysis and simulation tests, and finally a real fMRI data test is presented.
文摘为进一步提高变压器机械故障智能诊断的准确性,文中基于变压器振动信号时间序列符号化的模式表征,提出了一种基于栈式自编码器的变压器机械故障诊断模型。首先对振动信号时间序列进行符号化模式表征和构建复杂网络,提取了基于度分布的变压器振动信号特征向量,据此构建了基于栈式自编码器(stacked auto encoder,SAE)的变压器机械故障诊断模型。对某10 k V干式变压器正常与典型机械故障下振动信号的分析结果表明,变压器振动信号时间序列的符号化模式表征及度分布能较好地表征其动力学特征,所构建的基于SAE变压器机械故障模型具有较高的识别准确率,可达95%,研究结果可为变压器的机械故障诊断提供新思路。
文摘采用示踪技术探索了3 H JA的运输和分配规律及其受伤害胁迫的影响。外源 3 H JA能够在小麦幼苗体内向上和向下运输 ,局部灼伤其运输与分配都发生了改变。从小麦根系饲喂的3 H JA ,在植株内的分布量依序为根 >茎 >叶 ,时间较长 (4h)时分配于心叶的3 H JA大大增加。当叶片受到局部灼伤时3 H JA向地上部的输出量减少 ;但局部灼伤可加快由心叶饲喂的3 H JA的向下运输 ,改变3 H JA在小麦幼苗各部位的分配比率。心叶饲喂短时间 (5min)时 ,3 H JA主要积累在受到伤胁迫的展开叶 (第 2叶 )中。向展开叶 (第2叶 )饲喂的3 H JA向下运输的速率高于向上运输的速率。
