Maximum entropy spectral analysis of hydrologic data
Maximum entropy spectral analysis of hydrologic data
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DOI：
10.1029/wr024i009p01519
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发表时间：
1988-09
影响因子：
5.4
通讯作者：
G. Padmanabhan;A. Rao
中科院分区：
文献类型：
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作者：
G. Padmanabhan;A. Rao
周期的确定是水文时间序列随机分析中经常遇到的问题。传统的谱分析方法，如目前在水文数据分析中使用的那些方法，有一些长期公认的缺点。最大熵谱分析（梅萨）方法是作为传统谱分析的替代方法而发展起来的，在勘探地球物理学中受到了相当大的关注。本文讨论了梅萨在水文时间序列中应用的几个方面。梅萨方法的性能进行了比较，与常用的Blackman和Tukey方法。梅萨可以扩展到获得额外的随机特性，如自相关，偏自相关，逆自相关，逆偏自相关和时间序列数据的交叉谱。因此，谱分析和计算有关的随机模式的发展，可以集成使用梅萨。最大熵和最大似然谱之间的简单关系可以用于过滤我们的伪周期，所述伪周期发生在通过使用大滤波器阶数估计的最大熵谱中。
Determination of periodicities is a frequently encountered problem in stochastic analysis of hydrologic time series data. Conventional methods of spectral analysis such as those currently used in the analysis of hydrologic data have some long recognized disadvantages. The maximum entropy spectral analysis (MESA) method was developed as an alternative to conventional spectral analysis and has received considerable attention in exploratory geophysics. Several aspects of application of MESA to hydrologic time series are discussed in this paper. The performance of the MESA method is compared with the commonly used Blackman and Tukey method. MESA can be extended to obtain additional stochastic characteristics such as autocorrelation, partial autocorrelation, inverse autocorrelation, inverse partial autocorrelation, and cross spectra of time series data. Thus spectral analysis and computations related to stochastic model development may be integrated by using MESA. The simple relationship between maximum entropy and maximum likelihood spectra may be used to filter our pseudoperiodicities that occur in maximum entropy spectra estimated by using large filter orders.