scholarly journals Direct inversion of the apparent complex‐resistivity spectrum

Geophysics ◽  
2001 ◽  
Vol 66 (5) ◽  
pp. 1399-1404 ◽  
Author(s):  
J. Xiang ◽  
N. B. Jones ◽  
D. Cheng ◽  
F. S. Schlindwein
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Optimal Parameter ◽  
Geophysical Methods ◽  
Inversion Method ◽  
Model Parameters ◽  
Complex Resistivity ◽  
Direct Inversion ◽  
Optimal Parameter Estimation ◽  
Cole Model

Cole‐Cole model parameters are widely used to interpret electrical geophysical methods and are obtained by inverting the induced polarization (IP) spectrum. This paper presents a direct inversion method for parameter estimation based on multifold least‐squares estimation. Two algorithms are described that provide optimal parameter estimation in the least‐squares sense. Simulations demonstrate that both algorithms can provide direct apparent spectral parameter inversion for complex resistivity data. Moreover, the second algorithm is robust under reasonably high noise.

Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network

2021 ◽  
Vol 26 (1) ◽  
pp. 71-77
Author(s):  
Weiqiang Liu ◽  
Rujun Chen ◽  
Liangyong Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parameter Estimation ◽  
Model Parameters ◽  
Near Surface ◽  
Complex Resistivity ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Artificial Neural ◽  
Cole Model

In near surface electrical exploration, it is often necessary to estimate the Cole-Cole model parameters according to the measured multi-frequency complex resistivity spectrum of ore and rock samples in advance. Parameter estimation is a nonlinear optimization problem, and the common method is least square fitting. The disadvantage of this method is that it relies on initial value and the result is unstable when data is confronted with noise interference. To further improve the accuracy of parameter estimation, this paper applied artificial neural network (ANN) method to the Cole-Cole model estimation. Firstly, a large number of forward models are generated as samples to train the neural network and when the data fitting error is lower than the error threshold, the training ends. The trained neural network is directly used to efficiently estimate the parameters of vast amounts of new data. The efficiency of the artificial neural network is analyzed by using simulated and measured spectral induced polarization data. The results show that artificial neural network method has a faster computing speed and higher accuracy in Cole-Cole model parameter estimation.

Parameter Estimation and Sensitivity Analysis of a Nonlinearly Elastic Static Lung Model

1985 ◽  
Vol 107 (4) ◽  
pp. 315-320 ◽  
Author(s):  
J. R. Ligas ◽  
G. M. Saidel ◽  
F. P. Primiano
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Least Squares ◽  
Static Pressure ◽  
Optimal Parameter ◽  
Elastic Strain Energy ◽  
Lung Model ◽  
Model Parameters ◽  
Least Squares Fit ◽  
Parameter Values

A model for the static pressure-volume behavior of the lung parenchyma based on a pseudo-elastic strain energy function was tested. Values of the model parameters and their variances were estimated by an optimal least-squares fit of the model-predicted pressures to the corresponding data from excised, saline-filled dog lungs. Although the model fit data from twelve lungs very well, the coefficients of variation for parameter values differed greatly. To analyze the sensitivity of the model output to its parameters, we examined an approximate Hessian, H, of the least-squares objective function. Based on the determinant and condition number of H, we were able to set formal criteria for choosing the most reliable estimates of parameter values and their variances. This in turn allowed us to specify a normal range of parameter values for these dog lungs. Thus the model not only describes static pressure-volume data, but also uses the data to estimate parameters from a fundamental constitutive equation. The optimal parameter estimation and sensitivity analysis developed here can be widely applied to other physiologic systems.

scholarly journals Optimal Parameter Estimation for Muskingum Model Using a CSS-PSO Method

2013 ◽  
Vol 5 ◽  
pp. 480954 ◽  
Author(s):  
S. Talatahari ◽  
R. Sheikholeslami ◽  
B. Farahmand Azar ◽  
H. Daneshpajouh
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Muskingum Model ◽  
Optimal Parameter Estimation

Retrieval of Height Profiles of Trace Gases by Optimal Parameter Estimation

1997 ◽  
pp. 285-291
Author(s):  
E. Scheer ◽  
H. G. Bock ◽  
U. Platt ◽  
R. Rannacher
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Trace Gases ◽  
Optimal Parameter Estimation
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