Automatic Estimation of Regularization Parameter by Unbiased Predictive Risk Estimator (UPRE) Method in 3-D Constrained Inversion of Magnetic Data

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Rezaie Mohammad; MORADZADEH ALI; NEJATI KALATEH ALI; AGHAJANI HAMID

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Journal: JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS Year:2018 | Volume:3 | Issue:2 Page(s): 145-154

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Title

Automatic Estimation of Regularization Parameter by Unbiased Predictive Risk Estimator (UPRE) Method in 3-D Constrained Inversion of Magnetic Data

Author(s)

Rezaie Mohammad | MORADZADEH ALI | NEJATI KALATEH ALI | AGHAJANI HAMID | Issue Writer Certificate

Keywords

3D inversion

Magnetic data

Regularization parameter

UPRE method

GPRN method

Model parameter

Allah Abad iron deposit

Abstract

Inversion of Magnetic data is one of the important steps in the interpretation of practical Magnetic data. The inversion result can be obtained by minimization of Tikhonov objective function. The determination of an optimal Regularization parameter is highly important in Magnetic data inversion. In this paper, an attempt has been made to use unbiased predictive risk estimator (UPRE) method in selecting the best Regularization parameter for 3D constrained inversion of Magnetic data using gradient projection reduced Newton (GPRN) algorithm. To achieve this goal, an algorithm has been developed to estimate this parameter. The validity of the proposed algorithm has been evaluated by Magnetic data acquired from a synthetic model. The results have been compared with the results of generalized cross validation (GCV) method. The GCV method failed to estimate the Regularization parameter, but the UPRE method could find the best Regularization parameter. Then, the algorithm was used for inversion of real Magnetic data obtained from Allah Abad iron deposit. The results of three-dimensional (3-D) inversion of Magnetic data from this iron deposit show that the GPRN algorithm can provide an adequate estimate of magnetic susceptibility and geometry of subsurface structures of mineral deposits. A comparison of the inversion results with drilling data clearly indicate that the proposed algorithm can be used for 3-D inversion of Magnetic data to estimate precisely the magnetic susceptibility and geometry of magnetized ore bodies.

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APA: Copy

Rezaie, Mohammad, MORADZADEH, ALI, NEJATI KALATEH, ALI, & AGHAJANI, HAMID. (2018). Automatic Estimation of Regularization Parameter by Unbiased Predictive Risk Estimator (UPRE) Method in 3-D Constrained Inversion of Magnetic Data. JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS, 3(2 ), 145-154. SID. https://sid.ir/paper/268605/en

Vancouver: Copy

Rezaie Mohammad, MORADZADEH ALI, NEJATI KALATEH ALI, AGHAJANI HAMID. Automatic Estimation of Regularization Parameter by Unbiased Predictive Risk Estimator (UPRE) Method in 3-D Constrained Inversion of Magnetic Data. JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS[Internet]. 2018;3(2 ):145-154. Available from: https://sid.ir/paper/268605/en

IEEE: Copy

Mohammad Rezaie, ALI MORADZADEH, ALI NEJATI KALATEH, and HAMID AGHAJANI, “Automatic Estimation of Regularization Parameter by Unbiased Predictive Risk Estimator (UPRE) Method in 3-D Constrained Inversion of Magnetic Data,” JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS, vol. 3, no. 2 , pp. 145–154, 2018, [Online]. Available: https://sid.ir/paper/268605/en

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