On the Role of Lag Selection in Minimum‐Relative‐Entropy Spectral Estimation

Kybernetes ◽  
1991 ◽  
Vol 20 (2) ◽  
pp. 35-48
Author(s):  
Rallis C. Papademetriou
Keyword(s):  
Relative Entropy ◽  
Spectral Estimation ◽  
Lag Selection ◽  
Minimum Relative Entropy

scholarly journals Adding Prior Information in FWI through Relative Entropy

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 599
Author(s):  
Danilo Cruz ◽  
João de Araújo ◽  
Carlos da Costa ◽  
Carlos da Silva
Keyword(s):  
Inverse Problem ◽  
Relative Entropy ◽  
Global Minimum ◽  
Prior Information ◽  
Waveform Inversion ◽  
Petroleum Industry ◽  
Synthetic Data ◽  
Full Waveform Inversion ◽  
Full Waveform

Full waveform inversion is an advantageous technique for obtaining high-resolution subsurface information. In the petroleum industry, mainly in reservoir characterisation, it is common to use information from wells as previous information to decrease the ambiguity of the obtained results. For this, we propose adding a relative entropy term to the formalism of the full waveform inversion. In this context, entropy will be just a nomenclature for regularisation and will have the role of helping the converge to the global minimum. The application of entropy in inverse problems usually involves formulating the problem, so that it is possible to use statistical concepts. To avoid this step, we propose a deterministic application to the full waveform inversion. We will discuss some aspects of relative entropy and show three different ways of using them to add prior information through entropy in the inverse problem. We use a dynamic weighting scheme to add prior information through entropy. The idea is that the prior information can help to find the path of the global minimum at the beginning of the inversion process. In all cases, the prior information can be incorporated very quickly into the full waveform inversion and lead the inversion to the desired solution. When we include the logarithmic weighting that constitutes entropy to the inverse problem, we will suppress the low-intensity ripples and sharpen the point events. Thus, the addition of entropy relative to full waveform inversion can provide a result with better resolution. In regions where salt is present in the BP 2004 model, we obtained a significant improvement by adding prior information through the relative entropy for synthetic data. We will show that the prior information added through entropy in full-waveform inversion formalism will prove to be a way to avoid local minimums.

scholarly journals M2-spectral estimation: A relative entropy approach

Automatica ◽  
2021 ◽  
Vol 125 ◽  
pp. 109404
Author(s):  
Bin Zhu ◽  
Augusto Ferrante ◽  
Johan Karlsson ◽  
Mattia Zorzi
Keyword(s):  
Relative Entropy ◽  
Spectral Estimation

Minimum Relative Entropy Criterion for Damage Detection and Location

2007 ◽  
pp. 421-426
Author(s):  
Andreas Kyprianou ◽  
Cristinel Mares ◽  
Charalambos D. Charalambous ◽  
John E. Mottershead
Keyword(s):  
Damage Detection ◽  
Relative Entropy ◽  
Minimum Relative Entropy ◽  
Entropy Criterion

Minimum relative entropy enhancement of FFT spectra

1990 ◽  
Vol 26 (14) ◽  
pp. 979 ◽  
Author(s):  
J.P. Noonan ◽  
M.A. Tzannes ◽  
B. Achour
Keyword(s):  
Relative Entropy ◽  
Minimum Relative Entropy
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