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Journal articles on the topic 'Levenberg Marquardt method'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wu, Zhenxiang, Tong Zhou, Lei Li, Liang Chen, and Yanfang Ma. "A New Modified Efficient Levenberg–Marquardt Method for Solving Systems of Nonlinear Equations." Mathematical Problems in Engineering 2021 (August 31, 2021): 1–11. http://dx.doi.org/10.1155/2021/5608195.

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For systems of nonlinear equations, a modified efficient Levenberg–Marquardt method with new LM parameters was developed by Amini et al. (2018). The convergence of the method was proved under the local error bound condition. In order to enhance this method, using nonmonotone technique, we propose a new Levenberg–Marquardt parameter in this paper. The convergence of the new Levenberg–Marquardt method is shown to be at least superlinear, and numerical experiments show that the new Levenberg–Marquardt algorithm can solve systems of nonlinear equations effectively.

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2

Izmailov, Alexey, Alexey Kurennoy, and Petr Stetsyuk. "Levenberg–Marquardt method for unconstrained optimization." Tambov University Reports. Series: Natural and Technical Sciences, no. 125 (2019): 60–74. http://dx.doi.org/10.20310/1810-0198-2019-24-125-60-74.

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We propose and study the Levenberg–Marquardt method globalized by means of linesearch for unconstrained optimization problems with possibly nonisolated solutions. It is well-recognized that this method is an efficient tool for solving systems of nonlinear equations, especially in the presence of singular and even nonisolated solutions. Customary globalization strategies for the Levenberg–Marquardt method rely on linesearch for the squared Euclidean residual of the equation being solved. In case of unconstrained optimization problem, this equation is formed by putting the gradient of the object

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3

He, Zhi, Lan Yun Li, and Yong Qin Liu. "Calculating the Phase Equilibria of Al Rich Al-Cu-Mg Alloys." Advanced Materials Research 287-290 (July 2011): 2411–14. http://dx.doi.org/10.4028/www.scientific.net/amr.287-290.2411.

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This paper investigates a new method, the Levenberg-Marquardt method, to calculate the phase equilibria of the Al-Cu-Mg ternary alloys. The Levenberg-Marquardt method is the best algorithm to obtain the least-square solution of non-linear equations. Its application to ternary Al-Cu-Mg system is executed in detail in this paper. The calculated phase equilibria agrees well with the experimental results. Furthermore, the Levenberg-Marquardt method is not sensitive to the initial values.

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4

Chen, Liang, and Yanfang Ma. "A New Modified Levenberg–Marquardt Method for Systems of Nonlinear Equations." Journal of Mathematics 2023 (February 10, 2023): 1–13. http://dx.doi.org/10.1155/2023/6043780.

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Taking a new choice of the LM parameter λ k = μ k J k T F k δ with δ ∈ 0,2 , we give a new modified Levenberg–Marquardt method. Under the error bound condition c dist w , S ≤ J w T F w which is weaker than the nonsingular of Jacobian J w , we present that the new Levenberg–Marquardt method has at least superlinear convergence when δ ∈ 0,1 and quadratic convergence when δ ∈ 1,2 , respectively, which indicates that our new Levenberg–Marquardt method is performed for the systems of nonlinear equations. Also, numerical experiments indicate its efficiency on a set of systems of nonlinear equations.

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5

Cho, Kar Mun, Nur Haizum Abd Rahman, and Iszuanie Syafidza Che Ilias. "Performance of Levenberg-Marquardt Neural Network Algorithm in Air Quality Forecasting." Sains Malaysiana 51, no. 8 (2021): 2645–54. http://dx.doi.org/10.17576/jsm-2022-5108-23.

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Levenberg-Marquardt algorithm and conjugate gradient method are frequently used for optimization in multi-layer perceptron (MLP). However, both algorithms have mixed conclusions in optimizing MLP in time series forecasting. This study uses autoregressive integrated moving average (ARIMA) and MLP with both Levenberg-Marquardt algorithm and conjugate gradient method. These methods were used to predict the Air Pollutant Index (API) in Malaysia's central region where represent urban and residential areas. The performances were discussed and compared using the mean square error (MSE) and mean absol

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6

Izmailov, Alexey F., and Evgeniy I. Uskov. "Hybrid globalization of convergence of the Levenberg-Marquardt method for equality-constrained optimization problems." Russian Universities Reports. Mathematics, no. 149 (2025): 41–55. https://doi.org/10.20310/2686-9667-2025-30-149-41-55.

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The Levenberg-Marquardt method possesses local superlinear convergence for general systems of nonlinear equations under weal assumptions allowing for nonisolated solutions. This justifies its application to first-order optimality systems of constrained optimization problems with possibly violated constraint qualifications, the latter giving rise to nonuniqueness of Lagrange multipliers. However, the existing strategies for globalization of convergence of the Levenberg-Marquardt method are not optimization-oriented by nature, i.e., when applied to optimization problems, they are intended not fo

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7

Du, Shou-qiang, and Yan Gao. "A Modified Levenberg-Marquardt Method for Nonsmooth Equations with Finitely Many Maximum Functions." Mathematical Problems in Engineering 2008 (2008): 1–10. http://dx.doi.org/10.1155/2008/942391.

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For solving nonsmooth systems of equations, the Levenberg-Marquardt method and its variants are of particular importance because of their locally fast convergent rates. Finitely many maximum functions systems are very useful in the study of nonlinear complementarity problems, variational inequality problems, Karush-Kuhn-Tucker systems of nonlinear programming problems, and many problems in mechanics and engineering. In this paper, we present a modified Levenberg-Marquardt method for nonsmooth equations with finitely many maximum functions. Under mild assumptions, the present method is shown to

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8

Fan, Jinyan, and Jianyu Pan. "Inexact Levenberg-Marquardt method for nonlinear equations." Discrete & Continuous Dynamical Systems - B 4, no. 4 (2004): 1223–32. http://dx.doi.org/10.3934/dcdsb.2004.4.1223.

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9

Fan, Jinyan, Jianchao Huang, and Jianyu Pan. "An Adaptive Multi-step Levenberg–Marquardt Method." Journal of Scientific Computing 78, no. 1 (2018): 531–48. http://dx.doi.org/10.1007/s10915-018-0777-8.

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10

Zhang, Ju-liang, and Xiangsun Zhang. "A smoothing Levenberg–Marquardt method for NCP." Applied Mathematics and Computation 178, no. 2 (2006): 212–28. http://dx.doi.org/10.1016/j.amc.2005.11.036.

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11

Luo, Xin-Long, Li-Zhi Liao, and Hon Wah Tam. "Convergence analysis of the Levenberg–Marquardt method." Optimization Methods and Software 22, no. 4 (2007): 659–78. http://dx.doi.org/10.1080/10556780601079233.

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12

Behling, R., A. Fischer, M. Herrich, A. Iusem, and Y. Ye. "A Levenberg-Marquardt method with approximate projections." Computational Optimization and Applications 59, no. 1-2 (2013): 5–26. http://dx.doi.org/10.1007/s10589-013-9573-4.

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13

Zhao, Jinyao, Xuejuan Zhang, and Jinling Zhao. "A Levenberg-Marquardt Method for Tensor Approximation." Symmetry 15, no. 3 (2023): 694. http://dx.doi.org/10.3390/sym15030694.

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This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for the nonlinear least square problem, to decompose large-scale tensors into the sum of the products of vector groups of a given scale, or to obtain a low-rank tensor approximation without losing too much accuracy. An Armijo-like rule of inexact line search is also introduced into this algorithm. The result of the tensor decomposition is adjustable, which implies that the decomposition can be specified according to the users’ requirements. The convergence is proved, and numerical experiments show tha

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14

Liu, Zhidong, Xiaoke Liu, and Tsung-Jen Shen. "Applicability of Common Algorithms in Species–Area Relationship Model Fitting." Diversity 14, no. 3 (2022): 212. http://dx.doi.org/10.3390/d14030212.

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The species–area relationship (SAR) describes a law of species richness changes as the sampling area varies. SAR has been studied for more than 100 years and is of great significance in the fields of biogeography, population ecology, and conservation biology. Accordingly, there are many algorithms available for fitting the SARs, but their applicability is not numerically evaluated yet. Here, we have selected three widely used algorithms, and discuss three aspects of their applicability: the number of iterations, the time consumption, and the sensitivity to the initial parameter setting. Our re

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15

Liu, Chengchang, Luo Luo, and John C. S. Lui. "An Enhanced Levenberg--Marquardt Method via Gram Reduction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 18 (2025): 18772–79. https://doi.org/10.1609/aaai.v39i18.34066.

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This paper studies the problem of solving the system of nonlinear equations. We propose the Gram-reduced Levenberg--Marquardt method, which reuses the Gram matrix. Our method has a global convergence guarantee without relying on any step of line-search or solving sub-problems. We show that our method takes a smaller computational complexity than existing Levenberg--Marquardt methods to find the stationary point of the square norm of the equations. We also show that the proposed method enjoys a local superlinear convergence rate under the non-degenerate assumption. Experiments are conducted on

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16

Li, Changyou, Kang An, and Kuisong Zheng. "The Levenberg–Marquardt Method for Acousto-Electric Tomography on Different Conductivity Contrast." Applied Sciences 10, no. 10 (2020): 3482. http://dx.doi.org/10.3390/app10103482.

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The stability and convergence performance of Levenberg–Marquardt method for acousto-electric tomography (AET) applied to different levels of conductivity contrast is studied in this paper. As a multi-physical imaging modality, acousto-electric tomography (AET) provides high spatial imaging resolution while also conserving the high contrast property of electrical impedance tomography. The Levenberg–Marquardt method is a well known iteration scheme which can be applied for the nonlinear problem of AET. However, the influence of the conductivity contrast on the stability and convergence performan

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17

Parker, Danny L., William G. Frazier, and Mathew A. Gray. "Damage Localization Using Levenberg-Marquardt Optimization." Key Engineering Materials 347 (September 2007): 95–100. http://dx.doi.org/10.4028/www.scientific.net/kem.347.95.

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In this paper, an optimal solution method is proposed for determining the location of change, i.e. damage, within a perturbed system utilizing a nonlinear pseudo-second order search algorithm based on function evaluations and gradient information. This method is applied to damped vibrating systems and utilizes stiffness matrix sensitivities to determine the direction of search within the estimation. The site of damage (location of change) is the solution which minimizes the error between the predicted and measured change. A by-product of the Levenberg- Marquardt algorithm is an estimation of t

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18

Zheng, Lin, Liang Chen, and Yanfang Ma. "A variant of the Levenberg-Marquardt method with adaptive parameters for systems of nonlinear equations." AIMS Mathematics 7, no. 1 (2021): 1241–56. http://dx.doi.org/10.3934/math.2022073.

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<abstract><p>The Levenberg-Marquardt method is one of the most important methods for solving systems of nonlinear equations and nonlinear least-squares problems. It enjoys a quadratic convergence rate under the local error bound condition. Recently, to solve nonzero-residue nonlinear least-squares problem, Behling et al. propose a modified Levenberg-Marquardt method with at least superlinearly convergence under a new error bound condtion <sup>[<xref ref-type="bibr" rid="b3">3</xref>]</sup>. To extend their results for systems of nonlinear equations, by choos

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19

Chen, Yuan-yuan, and Yan Gao. "Levenberg-Marquardt Method for the Eigenvalue Complementarity Problem." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/307823.

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The eigenvalue complementarity problem (EiCP) is a kind of very useful model, which is widely used in the study of many problems in mechanics, engineering, and economics. The EiCP was shown to be equivalent to a special nonlinear complementarity problem or a mathematical programming problem with complementarity constraints. The existing methods for solving the EiCP are all nonsmooth methods, including nonsmooth or semismooth Newton type methods. In this paper, we reformulate the EiCP as a system of continuously differentiable equations and give the Levenberg-Marquardt method to solve them. Und

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20

Felde, Imre. "Estimation of Thermal Boundary Conditions by Using Hybrid Inverse Approach." Materials Science Forum 812 (February 2015): 419–24. http://dx.doi.org/10.4028/www.scientific.net/msf.812.419.

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The estimation of thermal boundary conditions occurring during heat treatment processes is an essential requirement for characterization of heat transfer phenomena. In this work, the performance of five optimization techniques is studied. These models are the Conjugate Gradient Method, the Levenberg-Marquardt Method, the Simplex method, the NSGA II algorithm and a hybrid approach based on the NSGA II and Levenberg-Marquardt Method sequence. The models are used to estimate the heat transfer coefficient in 2D axis symmetrical case during transient heat transfer. The performance of the optimizati

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21

Guo, Huang, Bao Ru Han, and Guo Fang Zhang. "Analog Circuit Fault Diagnosis Based on Levenberg-Marquardt Learning Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 979–82. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.979.

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This paper presents a fault diagnosis method of BP neural network based on Levenberg-Marquardt learning algorithm. First, the use of principal component analysis to reduce the dimension of the fault sample reduced BP neural network input variables. Then use the Levenberg-Marquardt learning algorithm to adjust the network weights. Levenberg-Marquardt learning algorithm is combination of the Gauss - Newton algorithm and steepest descent algorithm. It has Gauss - Newton algorithm of local convergence and gradient descent algorithm of the global characteristic. So it has higher convergence speed,

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22

Huang, Jianchao. "Extended Levenberg-Marquardt Method for Composite Function Minimization." Journal of Computational Mathematics 35, no. 4 (2017): 529–46. http://dx.doi.org/10.4208/jcm.1702-m2016-0699.

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23

Sugihara, Tomomichi. "Solvability-unconcerned Inverse Kinematics by Levenberg-Marquardt Method." Journal of the Robotics Society of Japan 29, no. 3 (2011): 269–77. http://dx.doi.org/10.7210/jrsj.29.269.

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24

Ma, Chao, Xin Liu, and Zaiwen Wen. "Globally Convergent Levenberg-Marquardt Method for Phase Retrieval." IEEE Transactions on Information Theory 65, no. 4 (2019): 2343–59. http://dx.doi.org/10.1109/tit.2018.2881187.

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25

Oda, Kazuo, Tsuyoshi Kondo, Masayoshi Obata, and Takeshi Doihara. "Automated Image Mosaicing based on Levenberg-Marquardt method." Journal of the Japan society of photogrammetry and remote sensing 37, no. 3 (1998): 42–51. http://dx.doi.org/10.4287/jsprs.37.3_42.

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26

Kazemi, Parimah, and Robert J. Renka. "A Levenberg–Marquardt method based on Sobolev gradients." Nonlinear Analysis: Theory, Methods & Applications 75, no. 16 (2012): 6170–79. http://dx.doi.org/10.1016/j.na.2012.06.022.

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27

Watson, G. A. "A Levenberg–Marquardt method for estimating polygonal regions." Journal of Computational and Applied Mathematics 208, no. 2 (2007): 331–40. http://dx.doi.org/10.1016/j.cam.2006.10.001.

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28

Sulaiman, I. M., M. Mamat, M. Y. Waziri, M. A. Mohamed, and F. S. Mohamad. "SOLVING FUZZY NONLINEAR EQUATION VIA LEVENBERG-MARQUARDT METHOD." Far East Journal of Mathematical Sciences (FJMS) 103, no. 10 (2018): 1547–58. http://dx.doi.org/10.17654/ms103101547.

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29

Zhao, Ruixue, and Jinyan Fan. "Global complexity bound of the Levenberg–Marquardt method." Optimization Methods and Software 31, no. 4 (2016): 805–14. http://dx.doi.org/10.1080/10556788.2016.1179737.

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30

Boos, Everton, Douglas S. Gonçalves, and Fermín S. V. Bazán. "Levenberg-Marquardt method with singular scaling and applications." Applied Mathematics and Computation 474 (August 2024): 128688. http://dx.doi.org/10.1016/j.amc.2024.128688.

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31

Pujol, Jose. "The solution of nonlinear inverse problems and the Levenberg-Marquardt method." GEOPHYSICS 72, no. 4 (2007): W1—W16. http://dx.doi.org/10.1190/1.2732552.

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Although the Levenberg-Marquardt damped least-squares method is an extremely powerful tool for the iterative solution of nonlinear problems, its theoretical basis has not been described adequately in the literature. This is unfortunate, because Levenberg and Marquardt approached the solution of nonlinear problems in different ways and presented results that go far beyond the simple equation that characterizes the method. The idea of damping the solution was introduced by Levenberg, who also showed that it is possible to do that while at the same time reducing the value of a function that must

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32

Zhu, Dingyu, Yueting Yang, and Mingyuan Cao. "An accelerated adaptive two-step Levenberg–Marquardt method with the modified Metropolis criterion." AIMS Mathematics 9, no. 9 (2024): 24610–35. http://dx.doi.org/10.3934/math.20241199.

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<p>In this paper, aiming at the nonlinear equations, a new two-step Levenberg–Marquardt method was proposed. We presented a new Levenberg–Marquardt parameter to obtain the trial step. A new modified Metropolis criterion was used to adjust the upper bound of the approximate step. The convergence of the method was analyzed under the H$ \ddot{\rm o} $lderian local error bound condition and the H$ \ddot\rm o $lderian continuity of the Jacobian. Numerical experiments showed that the new algorithm is effective and competitive in the numbers of functions, Jacobian evaluations and iterations.&lt

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33

He, Xiaorui, and Jingyong Tang. "A smooth Levenberg-Marquardt method without nonsingularity condition for wLCP." AIMS Mathematics 7, no. 5 (2022): 8914–32. http://dx.doi.org/10.3934/math.2022497.

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<abstract><p>In this paper we consider the weighted Linear Complementarity Problem (wLCP). By using a smooth weighted complementarity function, we reformulate the wLCP as a smooth nonlinear equation and propose a Levenberg-Marquardt method to solve it. The proposed method differentiates itself from the current Levenberg-Marquardt type methods by adopting a simple derivative-free line search technique. It is shown that the proposed method is well-defined and it is globally convergent without requiring wLCP to be monotone. Moreover, the method has local sub-quadratic convergence rate

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34

Coelho, Alan Anthony. "Optimum Levenberg–Marquardt constant determination for nonlinear least-squares." Journal of Applied Crystallography 51, no. 2 (2018): 428–35. http://dx.doi.org/10.1107/s1600576718001784.

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A new method for determining an approximate optimum value for the Levenberg–Marquardt constant has been shown to improve the convergence rate of nonlinear least-squares problems including complex X-ray powder diffraction and single-crystal structural refinements. In the Gauss–Newton method of nonlinear least squares, a lower value for the objective function is occasionally not realized after solving the matrix equationAΔp=b. This situation occurs when either the objective function is at a minimum or theAmatrix is ill conditioned. Invariably the Levenberg–Marquardt method is used, where the mat

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35

Bilski, Jarosław, Jacek Smoląg, Bartosz Kowalczyk, Konrad Grzanek, and Ivan Izonin. "Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 13, no. 2 (2023): 45–61. http://dx.doi.org/10.2478/jaiscr-2023-0006.

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Abstract This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algor

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36

Zhao, Ligang, Hua Zheng, Hongyue Zhen, Li Xie, Yuan Xu, and Xianchao Huang. "Improvement of Fuzzy Newton Power Flow Convergence." Energies 16, no. 24 (2023): 8044. http://dx.doi.org/10.3390/en16248044.

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In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the probl

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37

Mustafa, Yaseen. "VIGOROUS 3D ANGULAR RESECTION MODEL USING LEVENBERG – MARQUARDT METHOD." Surveying and Geospatial Engineering Journal 1, no. 1 (2020): 08–13. http://dx.doi.org/10.38094/sgej113.

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The resection in 3D space is a common problem in surveying engineering and photogrammetry based on observed distances, angles, and coordinates. This resection problem is nonlinear and comprises redundant observations which is normally solved using the least-squares method in an iterative approach. In this paper, we introduce a vigorous angular based resection method that converges to the global minimum even with very challenging starting values of the unknowns. The method is based on deriving oblique angles from the measured horizontal and vertical angles by solving spherical triangles. The de

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38

Mustafa, Yaseen. "Vigorous 3D Angular Resection Model Using Levenberg – Marquardt Method." Journal of Applied Science and Technology Trends 3, no. 01 (2022): 08–13. http://dx.doi.org/10.38094/jastt301120.

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The resection in 3D space is a common problem in surveying engineering and photogrammetry based on observed distances, angles, and coordinates. This resection problem is nonlinear and comprises redundant observations which is normally solved using the least-squares method in an iterative approach. In this paper, we introduce a vigorous angular based resection method that converges to the global minimum even with very challenging starting values of the unknowns. The method is based on deriving oblique angles from the measured horizontal and vertical angles by solving spherical triangles. The de

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39

Zhang, Ju-Liang. "On the convergence properties of the Levenberg–Marquardt method." Optimization 52, no. 6 (2003): 739–56. http://dx.doi.org/10.1080/0233193031000163993.

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40

Aravkin, Aleksandr Y., Robert Baraldi, and Dominique Orban. "A Levenberg–Marquardt Method for Nonsmooth Regularized Least Squares." SIAM Journal on Scientific Computing 46, no. 4 (2024): A2557—A2581. http://dx.doi.org/10.1137/22m1538971.

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41

Sugihara, Tomomichi. "Solvability-Unconcerned Inverse Kinematics by the Levenberg–Marquardt Method." IEEE Transactions on Robotics 27, no. 5 (2011): 984–91. http://dx.doi.org/10.1109/tro.2011.2148230.

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42

Song, Linsen, and Yan Gao. "A nonsmooth Levenberg-Marquardt method for vertical complementarity problems." Numerical Algorithms 76, no. 2 (2017): 473–85. http://dx.doi.org/10.1007/s11075-017-0267-3.

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43

Song, Linsen, and Yan Gao. "A smoothing Levenberg-Marquardt method for nonlinear complementarity problems." Numerical Algorithms 79, no. 4 (2018): 1305–21. http://dx.doi.org/10.1007/s11075-018-0485-3.

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44

Fan, Jinyan. "Accelerating the modified Levenberg-Marquardt method for nonlinear equations." Mathematics of Computation 83, no. 287 (2013): 1173–87. http://dx.doi.org/10.1090/s0025-5718-2013-02752-4.

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45

SAKAMOTO, H. "Acceleration and Stabilization Techniques for the Levenberg-Marquardt Method." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E88-A, no. 7 (2005): 1971–78. http://dx.doi.org/10.1093/ietfec/e88-a.7.1971.

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46

Yang, Xiao. "A higher-order Levenberg–Marquardt method for nonlinear equations." Applied Mathematics and Computation 219, no. 22 (2013): 10682–94. http://dx.doi.org/10.1016/j.amc.2013.04.033.

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47

Huang, Jian-Chao, and Jin-Yan Fan. "Global Complexity Bound of the Inexact Levenberg–Marquardt Method." Journal of the Operations Research Society of China 6, no. 3 (2017): 417–28. http://dx.doi.org/10.1007/s40305-017-0184-0.

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48

Fan, Jinyan. "A Shamanskii-like Levenberg-Marquardt method for nonlinear equations." Computational Optimization and Applications 56, no. 1 (2013): 63–80. http://dx.doi.org/10.1007/s10589-013-9549-4.

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49

Dong, Jiyang, Ke Lu, Jian Xue, Shuangfeng Dai, Rui Zhai, and Weiguo Pan. "Accelerated nonrigid image registration using improved Levenberg–Marquardt method." Information Sciences 423 (January 2018): 66–79. http://dx.doi.org/10.1016/j.ins.2017.09.059.

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50

Munadi, Suprajitno. "Avo Inversion Using Levenberg-Marquardt Optimization Technique." Scientific Contributions Oil and Gas 33, no. 2 (2022): 98–105. http://dx.doi.org/10.29017/scog.33.2.812.

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Abstract:

AVO is not only well known as gas indicator over the last two decades but also more importantly, AVO provides us with a means for extracting petrophysical parameters from seismic data. Using AVO anomaly one can derive important petrophysical parameters such as Poisson’s ratio and S-wave velocity. By knowing S-wave velocity nearly all other petrophysical parameters can be calculated. An effective procedure for inverting AVO anomaly is presented in this paper. It avoids inefficient trial and error steps during the matching process between AVO anomaly and calculated AVO. This method uses Levenber

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