Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when tradit...Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.展开更多
采用人工气候箱内培养皿培养,PEG-6000溶液模拟干旱胁迫环境,在萌发期以80、120、150和175 g L–1PEG-6000水溶液处理31个高粱品种,旨在根据高粱品种萌发期对不同干旱胁迫程度的响应,筛选出具有抗旱能力的高粱品种并探讨高粱萌发期抗旱...采用人工气候箱内培养皿培养,PEG-6000溶液模拟干旱胁迫环境,在萌发期以80、120、150和175 g L–1PEG-6000水溶液处理31个高粱品种,旨在根据高粱品种萌发期对不同干旱胁迫程度的响应,筛选出具有抗旱能力的高粱品种并探讨高粱萌发期抗旱性鉴定的方法。通过主成分分析法(PCA)和神经网络自组织映射(SOM)聚类分析法对各高粱品种进行抗旱性综合分析与评定。PCA结果表明,相对芽长、相对根长和相对萌发抗旱指数载荷量最大,将其作为萌发期高粱抗旱性筛选的主要评价指标,并对31个高粱品种抗旱性排序。通过SOM聚类分析将31个高粱品种按抗旱性强弱分为5类,吉杂305等4个品种为高度抗旱品种,HL5等4个品种为抗旱品种,辽杂10号等8个品种为中等抗旱品种,锦杂103等7个品种为干旱敏感品种,锦杂93等8个品种为高度干旱敏感品种。研究认为,相对芽长、相对根长和相对萌发抗旱指数等可以作为高粱品种抗旱性鉴定的重要指标;SOM聚类分析可作为品种抗旱性分类的重要方法。展开更多
基金supported by the Scientific Research Staring Foundation of University of Electronic Science and Technology of China(No.ZYGX2015KYQD049)
文摘Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.
文摘采用人工气候箱内培养皿培养,PEG-6000溶液模拟干旱胁迫环境,在萌发期以80、120、150和175 g L–1PEG-6000水溶液处理31个高粱品种,旨在根据高粱品种萌发期对不同干旱胁迫程度的响应,筛选出具有抗旱能力的高粱品种并探讨高粱萌发期抗旱性鉴定的方法。通过主成分分析法(PCA)和神经网络自组织映射(SOM)聚类分析法对各高粱品种进行抗旱性综合分析与评定。PCA结果表明,相对芽长、相对根长和相对萌发抗旱指数载荷量最大,将其作为萌发期高粱抗旱性筛选的主要评价指标,并对31个高粱品种抗旱性排序。通过SOM聚类分析将31个高粱品种按抗旱性强弱分为5类,吉杂305等4个品种为高度抗旱品种,HL5等4个品种为抗旱品种,辽杂10号等8个品种为中等抗旱品种,锦杂103等7个品种为干旱敏感品种,锦杂93等8个品种为高度干旱敏感品种。研究认为,相对芽长、相对根长和相对萌发抗旱指数等可以作为高粱品种抗旱性鉴定的重要指标;SOM聚类分析可作为品种抗旱性分类的重要方法。
文摘为了增强自组织映射(self-organizing map,SOM)网络的动态竞争和聚类能力,提高解的精度,在无监督的SOM神经网络的基础上,通过拓广获胜节点的数量,改进网络中的邻域函数和连接权函数等方法,提出具有多获胜节点的SOM模型.为了避免多个输入样本映射到同一个输出节点,还提出了禁忌映射的方法.为了验证所提出的方法的有效性,以股票的聚类分析为实例,对该方法进行了检验.通过对每股收益、每股净资产、净资产收益率、每股经营性现金流量及净利润等5项反映上市公司综合盈利能力的财务指标进行了模拟实验,所得的数值结果表明,在标准SOM及所提出的几种多获胜节点SOM网络模型中,具有双获胜节点(SOM with 2 winners,SOM2W)的网络模型获得了最好的聚类效果.结合实验结果对网络模型的进一步分析也表明,SOM2W的聚类能力优于标准SOM及其他网络模型.该模型为股票的分析和选择提供了一种可行的途径,在金融领域具有潜在的应用价值.
