scholarly journals Petrophysical parameters estimation using Levenberg-Marquardt and Singular Value Decomposition inversion schemes

2021 ◽  
Vol 11 (5) ◽  
pp. 24-38
Author(s):  
Moataz Mohamed Gomaa Abdelrahman ◽  
Norbert Péter Szabó ◽  
Mihály Dobróka
Keyword(s):  
Singular Value Decomposition ◽  
Synthetic Data ◽  
Data Interpretation ◽  
Singular Value ◽  
Parameters Estimation ◽  
Petrophysical Parameters ◽  
Inversion Methods ◽  
Levenberg Marquardt ◽  
Robustness To Noise ◽  
Value Decomposition

Well logging inversion was carried out using Levenberg-Marquardt (LM) and Singular Value Decomposition (SVD) techniques for the determination of petrophysical parameters, respectively. In this research, synthetic data contaminated with 5% Gaussian noise, and field data were used to compare the results from the two inversion methods. MATLAB software has been developed to solve the overdetermined inverse problem. The estimated petrophysical parameters from both inversion methods had been compared to one another in terms of robustness to noise, rock interface differentiation, different fluid prediction, and the accuracy of the estimated parameters. This research returns the reason to the inner iterative loop which is considered more about the Jacobian matrix sensitivity. The inversion results showed that both methods can be used in petrophysical data estimation for a reliable well-log data interpretation.

Microseismic denoising assessment by polarization histograms

Geophysics ◽  
2020 ◽  
pp. 1-46
Author(s):  
German I. Brunini ◽  
Juan I. Sabbione ◽  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Singular Value Decomposition ◽  
Quality Factor ◽  
Field Data ◽  
Synthetic Data ◽  
Singular Value ◽  
Mode Decomposition ◽  
Actual Field ◽  
Band Pass ◽  
Value Decomposition ◽  
Better Than

We present a comparison of microseismic data denoising methods based on their effect on the polarization attributes of 3C microseismic signals. The compared denoising methods include the classical band-pass filtering, and three recently proposed denoising techniques: restricted domain hyperbolic Radon transform denoising, singular value decomposition-based reduced-rank filtering, and empirical mode decomposition denoising. In order to draw the comparison, we have denoised 3C synthetic data contaminated with noise extracted from actual field data records, calculated their rectilinearity, azimuth, and dip polarization attributes, and arranged them into histograms. The comparison has been drawn by measuring the distances between the polarization histograms of the clean and denoised data, assuming that one method outperforms another if the aforementioned distance is smaller. This strategy allows to quantify the improvement in the calculated polarization attributes due to the different denoising processes. In addition, we have also calculated the quality factor of the denoised signals, which adds value and robustness to the comparison. Our results have indicated that the method based on singular value decomposition preserves the original polarization attributes better than the other techniques tested in this work. Moreover, it has also retrieved the denoised signal with the highest quality factor. Finally, we have tested the methods with field data and assessed their performance qualitatively on the basis of the insight gained from the numerical tests with synthetic data.

Structural Physical Parameter Identification of Nonlinear Parametric System in Time Domain

2011 ◽  
Vol 94-96 ◽  
pp. 1040-1043
Author(s):  
Xiang Jian Wang ◽  
Jie Cui
Keyword(s):  
Singular Value Decomposition ◽  
Parameter Identification ◽  
Physical Parameter ◽  
High Precision ◽  
Time Domain ◽  
Singular Value ◽  
Parametric System ◽  
Levenberg Marquardt ◽  
Shear Type ◽  
Value Decomposition

The modified Levenberg-Marquardt (mLM) method is introduced for nonlinear parametric system, such as stiffness proportional damping and Rayleigh proportional damping. Since the mLM method is sensitive to the initial values of parameter, a SVD-mLM method is proposed with combination of singular value decomposition (SVD). Parameter identification of five-storey shear-type is simulated with incomplete output. The results show that the identified parameters have high precision, and the proposed method is effective and robust on noise.

scholarly journals Improving Levenberg-Marquardt Algorithm Inversion Result Using Singular Value Decomposition

2016 ◽  
Vol 5 (2) ◽  
pp. 20
Author(s):  
Widodo Widodo ◽  
Durra Handri Saputera
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Parameter Analysis ◽  
Model Parameters ◽  
Inversion Result ◽  
One Dimensional ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Value Decomposition ◽  
Lm Algorithm

Inversion is a process to determine model parameters from data. In geophysics this process is very important because subsurface image is obtained from this process. There are many inversion algorithms that have been introduced and applied in geophysics problems; one of them is Levenberg-Marquardt (LM) algorithm. In this paper we will present one of LM algorithm application in one-dimensional magnetotelluric (MT) case. The LM algorithm used in this study is improved version of LM algorithm using singular value decomposition (SVD). The result from this algorithm is then compared with the algorithm without SVD in order to understand how much it has been improved. To simplify the comparison, simple synthetic model is used in this study. From this study, the new algorithm can improve the result of the original LM algorithm. In addition, SVD is allowing more parameter analysis to be done in its process. The algorithm created from this study is then used in our modeling program, called MAT1DMT.

Research on Aircraft Magnetic Compensation Based on Improved Singular Value Decomposition

2013 ◽  
Vol 281 ◽  
pp. 41-46 ◽  
Author(s):  
Jian Zhang ◽  
Peng Deng ◽  
Chun Sheng Lin
Keyword(s):  
Singular Value Decomposition ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Singular Value ◽  
Parameters Estimation ◽  
Interference Model ◽  
Estimation Accuracy ◽  
Regression Parameters ◽  
Magnetic Compensation ◽  
Value Decomposition

In aeromagnetic detection, the estimation of the regression parameters in aircraft magnetic interference model is the key of aircraft magnetic compensation. Taking aim at the multicollinearity of aircraft magnetic interference model, a new parameters estimation method with combination of wavelet threshold denoising and singular value decomposition (SVD) was proposed, which is called WSVD. First, wavelet threshold denoising was used to pretreat magnetometer data in order to decrease the noise of electric equipments which will influence the accuracy of parameters estimation. Then SVD was used in the estimation of regression parameters. In a simulation example, the estimation accuracy of LS estimation, SVD and WSVD was compared in different signal to noise ratio (SNR). The result shows that WSVD is more accurate than other two methods, especially more adaptive in low SNR. And the regression parameters of WSVD is effective in aircraft magnetic compensation. The compensation ratio is above 90%.

scholarly journals TerraSAR-X StripMap Data Interpretation of Complex Urban Scenarios with 3D SAR Tomography

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lianhuan Wei ◽  
Timo Balz ◽  
Mingsheng Liao ◽  
Lu Zhang
Keyword(s):  
Singular Value Decomposition ◽  
Urban Areas ◽  
Data Interpretation ◽  
Singular Value ◽  
Robust Approach ◽  
Butterworth Filter ◽  
Sar Data ◽  
Value Decomposition ◽  
Areas Of Interest ◽  
Tomographic Analysis

The severe layover problem of complex urban scenarios in SAR data makes SAR data interpretation very difficult, especially for nonexperts. In this paper, we use 3D SAR tomography for SAR data interpretation of dense urban areas. An efficient and robust approach named Butterworth-filter based singular value decomposition (BSVD) is used for tomographic analysis. Two typical dense urban areas of interest located in Shanghai are analyzed. The tomographic results could help users to better understand the backscattering scenario. The experimental results indicate that SAR tomography is a promising and effective way to facilitate SAR data interpretation of complex urban areas.

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