Pareto-optimal solutions of the inverse gravimetric problem in the class of three-dimensional contact surfaces
DOI:
https://doi.org/10.24028/gzh.0203-3100.v42i6.2020.222297Keywords:
inversion of gravimetric data, a priori information, Pareto-optimal solution, fuzzy setAbstract
In geophysical inverse problems, there are two approaches to data inversion. The first is the search for a number of unknowns by minimizing the residual function. The second is through probabilistic modeling of the posteriori of the probability density function in the framework of the Bayesian interpretation of the inverse problem. In most cases, the data—model relationship is non-linear, and the corresponding minimization or modeling becomes difficult due to the multimodality of the residual function.
This article discusses an approach related to improbability methods for solving inverse problems of geophysics. Its essence consists in direct modeling of a parametric space with a further search for Pareto-optimal solutions based on a priori information. A priori information is formalized through fuzzy sets. The model example demonstrates the use of the improbable direct search and the gradient method of speedy descent in solving the nonlinear gravimetric inverse problem in the class of three-dimensional contact surfaces, and also evaluates the effectiveness of both methods.
An analysis of the performed tests shows that if there is sufficient a priori information, both methods give a completely unambiguous accurate result. The search for Pareto-optimal solutions can have a faster convergence compared to the gradient descent method, although it is determined by many factors — the number of points of the initial population, the threshold value ε, and the required level of data correspondence. Also, the algorithm is resistant to falling into local minima, since it uniformly explores the parametric space.
The algorithm allows us to obtain completely satisfactory solutions already at the stage of searching for the initial Pareto set. This is a consequence of selective modeling under the control of a priori information. A subsequent direct search in the vicinity of the Pareto-optimal points leads to a significant decrease in the residual function and to the deviation of some local minima.
In conditions of a lack of a priori information, a set of Pareto-optimal solutions can serve as a basis for further extraction of useful data on anomalous sources using other geophysical interpretation methods.
We also note that the described approach to solving the inverse problem may be of interest in solving a wide range of other optimization geophysical problems.
References
Balk, P.I. (1980). On the reliability of the results of quantitative interpretation of gravity anomalies. Izv. Academy of Sciences of the USSR. Fizika Zemli, (6), 65-83 (in Russian).
Balk, P.I., & Dolgal, A.S. (2016). Minimization risk technique for solving gravity inverse problems in weak assumptions about geological noise properties. Geofizicheskiy zhurnal, 38(5), 108-118. https://doi.org/10.24028/gzh.0203-3100.v38i5.2016.107825 (in Russian).
Bulakh, E.G., & Kishman-Lavanova, T.N. (2006). Another approximation approach to solving inverse problems of gravimetry in the class of three-dimensional contact surfaces. Geofizicheskiy zhurnal, 28(2), 54-62 (in Russian).
Goltsman, F.M., & Kalinina, T.B. (1983). Statistical interpretation of magnetic and gravity anomalies. Leningrad: Nedra, 248 p. (in Russian).
Mudretsova, E.A. (Ed.). (1990). Gravity prospecting. Geophysics Handbook. Moscow: Nedra, 607 p. (in Russian).
Karataev, G.I., & Pashkevich, I.K. (1986). Geological and mathematical analysis of a complex of geophysical fields. Kiev: Naukova Dumka, 168 p. (in Russian).
Kishman-Lavanova, T.N. (2015). Pareto-optimal solutions of the inverse problem of gravimetry with indeterminate a priori information. Geofizicheskiy zhurnal, 37(5), 93-103. https://doi.org/10.24 028/gzh.0203-3100.v37i5.2015.111148 (in Russian).
Tikhonov, A.N., & Arsenin, V.Ya. (1986). Methods for solving ill-posed problems. Moscow: Nauka, 288 p. (in Russian).
Goldberg, D.E., Deb, K., Clark, J.H. (1992). Genetics algorithm, noise and the sizing of population. Complex Systems, 6, 333-362.
Kozlovskaya, E. (2000). An algorithm of geophysical data inversion based on non-probalistic presentation of a priori information and definition of Pareto-optimality. Inverse Problems, 16, 839-861.
Kozlovskaya, E., Vecsey, L., Plomerová, J., & Raita, T. (2007). Joint inversion of multiple data types with the use of multiobjective optimization: problem formulation and application to the seismic anisotropy investigations. Geophysical Journal International, 171(2), 761-779. https://doi.org/10.1111/j.1365-246X.2007.03540.x.
Parker, R.L. (1994). Geophysical Inverse Theory. Princeton University Press. 386 p.
Sambridge, M. (1999). Geophysical inversion with a neighbourhood algorithm - І. Searching a parameter space. Geophysical Journal International, 138(2), 479-494. https://doi.org/10.1046/j.1365-246X.1999.00876.x.
Sambridge, M., & Mosegaard, K. (2002). Monte Carlo methods in geophysical inverse problems. Reviews of Geophysics, 40(3), 301-329. https://doi.org/10.1029/2000RG000089.
Tarantola, A., & Valette, B. (1982). Generalized nonlinear inverse problems solved using the least squares criterion. Reviews of Geophysics, 20(2), 219-232. https://doi.org/10.1029/RG020i002p00219.
Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM, 341 p.
Voronoi, M.G. (1908). Nouvelles applications des parameters continues a la theorie des formes quadratiques. Journal fьr die reine und angewandte Mathematik, 134, 198-287.
Zhdanov, M. (2015). Inverse Theory and Applications in Geophysics. Elsevier Science, 730 p.
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