Experimental identification of IPMC actuator parameters through incorporation of linear and nonlinear least squares methods

2011 ◽  
Vol 168 (1) ◽  
pp. 140-148 ◽  
Author(s):  
Meisam Vahabi ◽  
Emad Mehdizadeh ◽  
Mansour Kabganian ◽  
Farshad Barazandeh
Keyword(s):  
Least Squares ◽  
Nonlinear Least Squares ◽  
Experimental Identification ◽  
Linear And Nonlinear

Automated spectral recomposition with application in stratigraphic interpretation

2013 ◽  
Vol 1 (1) ◽  
pp. SA109-SA116 ◽  
Author(s):  
Yihua Cai ◽  
Sergey Fomel ◽  
Hongliu Zeng
Keyword(s):  
Least Squares ◽  
Frequency Domain ◽  
Seismic Response ◽  
Cross Sections ◽  
Reservoir Characterization ◽  
Nonlinear Least Squares ◽  
Significant Frequency ◽  
Signal Parameters ◽  
Frequency Components ◽  
Linear And Nonlinear

Analyzing seismic attributes in the frequency domain is helpful for reservoir characterization. To analyze the reservoir interval of interest in detail, it is important to capture the seismic response at each frequency subset. Spectral recomposition can be used to extract significant components from the seismic spectrum. We propose a separable nonlinear least-squares algorithm for spectral recomposition, which estimates linear and nonlinear parts automatically in separate steps. Our approach is applied to estimate fundamental signal parameters, peak frequencies, and amplitudes, with which the seismic spectrum can be reconstructed. Automated spectral recomposition helps us visualize frequency-dependent geologic features on cross sections and time slices by extracting significant frequency components. Spectral recomposition can also indicate how frequency contents attenuate with time.

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