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برآورد پارامتر منظمسازی به‌روش متعادلسازی قید فعال در وارونسازی دو بعدی دادههای گرانی‌سنجی | ||
| فیزیک زمین و فضا | ||
| مقاله 6، دوره 44، شماره 3، آبان 1397، صفحه 575-583 اصل مقاله (696.39 K) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2018.252876.1006978 | ||
| نویسندگان | ||
| میثم مقدسی1؛ علی نجاتی کلاته* 2؛ محمد رضایی3 | ||
| 1دانشجوی کارشناسی ارشد، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| 2دانشیار، دانشکده مهندسی معدن، نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| 3استادیار، دانشکده فنی و مهندسی، دانشگاه ملایر، ملایر، ایران | ||
| چکیده | ||
| مدلسازی وارون دادههای گرانی، یکی از مؤثرترین ابزارهای عددی به‌منظور به‌دستآوردن تصاویر سهبعدی از ساختارهای زمین‌شناسی است. یکی از پارامترهای مؤثر برای تولید مدلی مناسب در مدلسازی معکوس دادههای گرانی همانند اغلب روشهای مدلسازی معکوس دادههای ژئوفیزیکی پارامتر منظمسازی است. روشهای مختلفی برای این پارامتر در وارونسازی دادههای گرانی مورد استفاده بوده است. در این مقاله از روش متعادلسازی قید فعال (ACB) به‌عنوان روشی جدید برای تخمین مناسب این پارامتر در وارونسازی دوبعدی دادههای گرانی‌سنجی پرداخته میشود. برای این منظور الگوریتم طراحی شده بر روی یک مدل مصنوعی و یک مجموعه دادههای واقعی گرانی‌سنجی مربوط به ذخیره کرومیت در منطقه ماتانزاس در کشور کوبا مورد مطالعه قرار گرفته است. نتایج حاصل از وارونسازی دوبعدی در این منطقه با حفاریهای موجود سازگاری دارند و نشان میدهد که الگوریتم پیشنهادی می‌‌تواند تخمین مناسبی از توزیع چگالی و ساختارهای زیرسطحی ماده معدنی ارائه کند. | ||
| کلیدواژه‌ها | ||
| وارونسازی؛ داده گرانی؛ پارامتر منظمسازی؛ روش (ACB) | ||
| عنوان مقاله [English] | ||
| Estimation of regularization parameter by active constraint balancing for 2D inversion of gravity data | ||
| نویسندگان [English] | ||
| Meysam Moghadasi1؛ Ali Nejati Kalateh2؛ Mohamad Rezaie3 | ||
| 1M.Sc. Student, Department of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| 2Associate Professor, Department of Petroleum and Geophysics, Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| 3Assistant Professor, Faculty of Civil Engineering, Malayer University, Malayer, Iran | ||
| چکیده [English] | ||
| Inversion method is very common in the interpretation of practical gravity data. The goal of 3D inversion is to estimate density distribution of an unknown subsurface model from a set of known gravity observations measured on the surface. The regularization parameter is one of the effective parameters for obtaining optimal model in inversion of the gravity data for similar inversion of other geophysical data. For estimation of the optimum regularization parameter the statistical criterion of Akaike’s Bayesian Information Criterion (ABIC) usually used. This parameter is experimentally estimated in most inversion methods. The choice of the regularization parameter, which balances the minimization of the data misfit and model roughness, may be a critical procedure to achieve both resolution and stability. In this paper the Active Constraint Balancing (ACB) as a new method is used for estimating the regularization parameter in two- dimensional (2-D) inversion of gravity data. This technique is supported by smoothness-constrained least-squares inversion. We call this procedure “active constraint balancing” (ACB). Introducing the Lagrangian multiplier as a spatially-dependent variable in the regularization term, we can balance the regularizations used in the inversion. Spatially varying Lagrangian multipliers (regularization parameters) are obtained by a parameter resolution matrix and Backus-Gilbert spread function analysis. For estimation of regularization parameter by ACB method use must computed the resolution matrix R. The parameter resolution matrix R can be obtained in the inversion process with pseudo-inverse multiplied by the kernel G. (1) The spread function, which accounts for the inherent degree of how much the ith model parameter is not resolvable, defined as: (2) where M is the total number of inversion parameters, is a weighting factor defined by the spatial distance between the ith and jth model parameters, and is a factor which accounts for whether the constraint or regularization is imposed on the ith parameter and its neighboring parameters. In other words, the spread function defined here is the sum of the squared spatially weighted spread of the ith model parameter with respect to all of the model parameters excluding ones upon which a smoothness constraint is imposed. In this approach, the regularization parameter λ(x,z) is set by a value from log-linear interpolation. (3) where and are the minimum and maximum values of spread function , respectively, and the and are minimum and maximum values of the regularization parameter λ(x,z), which must be provided by the user. With this method, we can automatically set a smaller value λ(x,z) of the regularization parameter to the highly resolvable model parameter, which corresponds to a smaller value of the spread function in the inversion process and vice versa. Users can choose these minimum and maximum regularization parameters by setting variables LambdaMin and LambdaMax. For getting the target an algorithm is developed that estimates this parameter. The validity of the proposed algorithm has been evaluated by gravity data acquired from a synthetic model. Then the algorithm used for inversion of real gravity data from Matanzas Cr deposit. The result obtained from 2D inversion of gravity data from this mine shows that this algorithm can provide good estimates of density anomalous structures within the subsurface. | ||
| کلیدواژه‌ها [English] | ||
| inversion, Gravity data, Regularization parameter, ACB method | ||
| مراجع | ||
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قائدرحمتی، ر.، مرادزاده، ع.، فتحیانپور، ن. و لی، س.، 1394، بهبود وارونسازی دوبعدی دادههای مگنتوتلوریک با استفاده از روشهای خودکار انتخاب پارامتر منظمسازی، مجله ژئوفیزیک ایران، 9 (1)، 45-30. Abedi, M., Gholami, A., Norouzi, G.-H. and Fathianpour, N., 2013, Fast inversion of magnetic data using Lanczos bidiagonalization method. Journal of Applied Geophysics, 90, 126–137. Aster, R. C., Borchers, B. and Thurber, C. H., 2013, Parameter Estimation and Inverse Problems 2, nd edition, Elsevier. Farquharson, C. G. and Oldenburg, D. W., 2004, A comparison of automatic techniques for estimating the regularization parameter in non-linear inverse problems. Geophys. J. Int., 156, 411–425. Flint, D. E., Francisco de Albear, J. and Guild, P. W., 1948, Geology and chromite deposits of the Camaguey district, Camaguey Province, Cuba :U. S., Geol. Survey Bull. 954-B, 61-62. Haber, E. and Oldenburg, D. W., 2000, A GCV based method for nonlinear ill-posed problems, Comput. Geosci., 4, 41–63. Hansen, P. C., 1997, Rank-defficient and discrete ill-posed problems. SIAM, Philadelphia, 14-44. Hansen, P. C., 2007, Regularization Tools :A Matlab Package for Analysis and Solution of Discrete Ill-Posed Problems Version 4:1 for Matlab 7:3 , Numerical Algorithms, 46, 189-194. Hansen, P. C., 2010, Discrete inverse problems: insight and algorithms 7, SIAM. Lee, S. K., Kim, H. J., Song, Y., Lee, C., 2009, MT2DInvMatlab- A program in MATLAB and FORTRAN for two dimensional magnetotelluric inversion, Computers and Geosciences, 35, 1722-1735. Li, Y. and Oldenburg, D. W., 1998, 3-D inversion of gravity data, Geophysics, 63(1), 109-119. Li, H., Xu, S., Yu, H., Wei, W. and Fang, J., 2010, Transformations between aeromagnetic gradients in frequency domain. Journal of Earth Science, 21(1), 114-122. Marquardt, D. W., 1970, Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation, Technometrics, 12 (3), 591-612. Menke, W., 1984, Geophysical Data Analysis: Discrete Inverse Theory. Academic Press, Inc. Nemeth, T., Normark, E. and Qin, F., 1997, Dynamic smoothing in crosswell traveltime tomography, Geophysics, 62, 168–176. Oldenburg, D. W. and Li, Y., 2005, Inversion for applied geophysics : A tutorial. Investigations in geophysics, 13, 89-150. Oliveira Jr, V. C., Barbosa, V. C. and Silva, J. B., 2011, Source geometry estimation using the mass excess criterion to constrain 3-D radial inversion of gravity data. Geophysical Journal International, 187(2), 754-772. Paige, C. C. and Saunders, M. A., 1982, LSQR: An algorithm for sparse linear equations and sparse least squares”, ACM Trans. Math. Soft. (TOMS), 8, 1, pp.43. Rezaie, M., Moradzadeh, A. and Kalateh, A. N., 2017, Fast 3D inversion of gravity data using solution space priorconditioned lanczos bidiagonalization. Journal of Applied Geophysics, 136, 42-50. Roy, L., Agarwal, B. N. P. and Shaw, R. K., 2000, A new concept in Euler deconvolution of isolated gravity anomalies. Geophys. Prospect. 48, 559–575. Santos, E. T. F. and Bassrei, A., 2007, Application of GCV in geophysical diffraction tomography. In :69th EAGE Conference and Exhibition, London. Sasaki, Y., 1994, 3D resistivity inversion using the finite element method. Geophysics 59, 1839-1848 Tikhonov, A. N. and Arsenin, V. Y., 1977, Solution of Ill-Posed Problems: V. H. Winston and Sons. Uieda, L. and Barbosa, V. C., 2012, Robust 3D gravity gradient inversion by planting anomalous densities, Geophysics, 77(4), G55-G66. Vatankhah, S., Ardestani, V. E., and Renaut, R. A., 2014, Automatic estimation of the regularization parameter in 2D focusing gravity inversion: application of the method to the Safo manganese mine in the northwest of Iran. Journal of Geophysics and Engineering, 11(4), 045001. Vatankhah, S., Ardestani, V. E. and Renaut, R. A., 2015, Application of the χ2 principle and unbiased predictive risk estimator for determining the regularization parameter in 3-D focusing gravity inversion, Geophysical Journal International, 200(1), 265-277. Wahba, G., 1990, Spline Models for Observational Data, vol. 59. SIAM, Philadelphia. Yi, M.-J., Kim, J.-H. and Chung, S.-H., 2003, Enhancing the resolving power of least-squares inversion with active constraint balancing. Geophysics 68, 931–941. | ||
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