Radyal Taban Fonksiyonlu Yapay Sinir Ağları (RTFA) ve Levenberg-Marquardt(LM) Ters Çözüm Yöntemleriyle Küre Şekilli Yapıların Doğal Uçlaşma Anomalilerinin Değerlendirilmesi

Yıl 2023, Cilt: 25 Sayı: 73, 159 - 166, 26.01.2023

Petek Sındırgı , İlknur Kaftan

https://doi.org/10.21205/deufmd.2023257313

Öz

Doğal uçlaşama (DU) uygulamalarında kaynak yapı özelliklerini belirlemek yöntemin temel amacıdır.
Çeşitli yöntemler bu özelliklerin saptanmasında kullanlmaktadır. Bu çalışmada Radyal Taban Fonksiyonlu Yapay Sinir Ağları (RTFA) ve geleneksel Levenberg-Marquardt (LM) ters çözüm yöntemleri DU verilerine uygulanmıştır. Çalışma iki aşamadan oluşmaktadır. İlk aşamada, gürültüsüz ve gürültülü küre şekilli kuramsal modelin DU anomalisinin her iki yöntemle ters çözümleri yapılarak model parametreleri saptanmıştır. İkinci aşamada ise yöntemler Seferihisar(İzmir) alanından toplanmış olan DU verilerine uygulanmıştır. Elde edilen sonuçlar karşılaştırıldığında, RTFA’nın LM ters çözüm sonuçlarına göre nispeten daha küçük hata değeriyle model parametrelerini saptadığı görülmüştür. Sonuç olarak, bu çalışma, RTFA yöntemi kullanılarak DU küre modeli ters çözümünün güvenilir bir şekilde yapılabileceğini ortaya koymuştur.

Anahtar Kelimeler

Doğal Uçlaşma(DU), Radyal Taban Fonksiyonlu Yapay Sinir Ağlar (RTFA), Levenberg-Marquardt(LM) ters çözümü

Evaluation of Self-Potential Anomalies caused by Sphere Shaped Structures with Radial Basis Function Neural Networks (RBFNN) and Levenberg-Marquardt(LM) Inversion Methods

Öz

The main purpose of the method is to determine the source structure properties in self-potential (SP) applications. Various methods are used to determine these properties. In this study, Radial Basis Function Neural Network (RBFANN) and traditional Levenberg-Marquardt (LM) inversion methods were applied to SP data. The study consists of two stages. In the first stage, the model parameters were determined by performing inverse solutions of the SP anomaly of the noise-free and noisy spherical polarized synthetic model with both methods. In the second stage, the methods were applied to the SP data collected from the Seferihisar (İzmir) field. When the results were compared, it was seen that RBFANN determined the model parameters with a relatively smaller error value than the LM inversion results. In conclusion, this study revealed that the SP sphere model inversion can be reliably performed using the RBFANN method.

Anahtar Kelimeler

Self Potential, Radial Basis Function Neural Network (RBFANN), Levenberg-Marquardt(LM) inversion

Kaynakça

• Yüngül, S.H. 1950. Interpretation of Spontaneous Polarization Anomalies Caused by Spheroidal Orebodies, Geophysics, Cilt. 15, s. 237-246.
• Paul, M.K. 1965. Direct Interpretation of Self-potential Anomalies Caused by Inclined Sheets of Infinite Extensions, Geophysics, Cilt. 30, s. 418-423.
• Bhattacharyya, B.B., Roy, N. 1981. A Note on the Use of a Nomogram for Self Potential Anomalies, Geophysical Prospecting, Cilt. 29, s. 102-107.
• Fitterman, D.V., Corwin, R.F. 1982. Inversion of Self-potential Data from the Cerro–Prieto Geothermal Field Mexico, Geophysics, Cilt. 47, s. 938-945.
• Ram Babu, H.V., Rao, D.A., 1988. Inversion of Self-potential Anomalies in Mineral Exploration, Computers and Geosciences, Cilt. 14, s.377-388.
• Abdelrahman, E.M., Sharafeldin, S. M. 1997. A Laest-squarea Approach to Depth Determination from Residual Self-potential Anomalies Caused by Horizontal Cylinders and Spheres, Geophysics Cilt. 62, s. 44-48.
• Shalivahan, B., Bhattacharya, B.B., Mrinal Sen, K. 1998. Interpretation of Self Potential Anomalies by Nonlinear Inversion, J. Geophy., Cilt. 19(4), s. 219-224.
• Asfahani, J., Tlas, M., 2002. A Nonlinear Programming Technique for the Interpretation of Self-potential Anomalies, Pure and Applied Geophysics, Cilt. 159, s. 1333-1343.
• El-Araby, H., 2004. A New Method for Quantitative Interpretation of Self-potential Anomalies, Journal of applied Geophysics, Cilt. 55, s.211-224.
• Essa, K., Mehanee, S., Smith. P.D. 2008. A New Inversion Algorithm for Estimating the Best Fitting Parameters of Some Geometrically Simple Body to Measured Self-potential Anomalies, Exploration Geophysics, Cilt. 39, s. 155-163.
• Pekşen, E., Yas, T., Kayman, A.Y., Özkan, C. 2011. Application of Particle Swarm Optimization on Self-potential Data, Journal of Applied Geophysics, Cilt. 75(2), 305-318.
• Abedi, M., Hafizi, M.K., Norouzi, G.H. 2012. 2D Interpretation of Self-potential Data Using Normalized Full Gradient, a Case Study: Galena Deposit, Bollettino di Geofisica Teorica ed Applicata, Cilt. 53, s. 213-230.
• Göktürkler, G., Balkaya, Ç., 2012, Inversion of Self-potential Anomalies Caused by Simple-geometry Bodies Using Global Optimization Algorithms, Journal of Geophysics and Engineering, Cilt. 9, s. 498-507. DOI:10.1088/1742-2132/9/5/498.
• Biswas, A., Sharma, S.P. 2014. Optimization of Self-Potential interpretation of 2-D Inclined Sheet-type Structures Based on Very Fast Simulated Annealing and Analysis of Ambiguity, Journal of Applied Geophysics, Cilt. 105, s. 235-247.
• Mehanee, S.A. 2015. Tracing of Paleo-shear Zones by Self-potential Data Inversion: Case Studies from the KTB, Rittsteig, and Grossensees Graphite-bearing Fault Planes, Earth, Planets and Space, Cilt. 67(1), s. 1-33. DOI 10.1186/s40623-014-0174-y
• Karaoğlan, S.T. , Göktürkler, G., 2021. Cuckoo Search Algorithm for Model Parameter Estimation from Self-potential Data, Journal of Applied Geophysics, Cilt. 194, s. 104461, DOI:10.1016/j.jappgeo.2021.104461
• Ekinci, Y. L., Balkaya, Ç., Göktürkler, G. 2020. Global Optimization of Near-Surface Potential Field Anomalies through Metaheuristics. ss 155-188.
• Biswas, A., Sharma, S., ed. 2020. Advances in Modeling and Interpretation in Near Surface Geophysics, Springer, Cham, 414s. DOI:10.1007/978-3-030-28909-6_7
• Sundararajan, N., Arun Kumar, I., Mohan, N.L., Seshagiri Rao, S.V. 1990. Use of the Hilbert Transform to Interpret Self-potential Anomalies due to Two-dimensional Inclined Sheets, Pure and Applied Geophysics, Cilt. 133, s. 117-126.
• Asfahani, J., Tlas, M., Hammadi, M. 2001. Fourier Analysis for Quantitative Interpretation of Sself-potential Anomalies Caused by Horizontal Cylinder and Sphere, Journal of King Abdulaziz University-Earth Sciences, Cilt. 13, s.41-53.
• Gilbert ,D., Pessel, M. 2001. Identification of Sources of Potential Fields with the Continuous Wavelet Transform: Application to Self-potential Profiles, Geophys. Res. Lett., Cilt. 28, s. 1863-1866.
• Al-Garni, M., Sundararajan, N. 2011. Hartley Spectral Analysis of Self-potential Anomalies Caused by a 2-D Horizontal Circular Cylinder, Arabian Journal of Geosciences, Cilt. 5(6) DOI: 10.1007/s12517-011-0285-8
• Di Maio, R., Piegari, E., Rani, P., Avella, A. 2016. Self-potential Data Inversion Through the Integration of Spectral Analysis and Tomographic Approaches, Geophysical Journal International, Cilt. 206, 1204-1220.
• Patella D (1997). Introduction to ground surface self-potential tomography. Geophysical Prospecting Cilt. 45, 653-681
• Revil, A., Ehouarne, L., Thyreault, E. 2001. Tomography of Self-potential Anomalies of Electrochemical Nature, Geophys. Res. Lett., Cilt. 28(23), s. 4363-4366.
• Juliano, T., Mauriello, P., Patella, D. 2002) Looking Inside Mount Vesuvius by Potential Fields Integrated Probability Tomographies, J. Volcanol. Geotherm. Res., Cilt. 113, s.363-378.
• El-Kaliouby, H.M., Al-Garni, M.A. 2009. Inversion of Self-potential Anomalies Caused by 2D Inclined Sheets using Neural Networks, J. Geophys. Eng., Cilt. 6, s. 29-34.
• Kaftan, İ., Sındırgı, P., Akdemir, Ö. 2014. Inversion of Self Potential Anomalies with Multilayer Perceptron Neural Networks, Pure and Applied Geophysics, Cilt. 171, s.1939-1949.
• Agarwal, B.N.P., Srivastava, S. 2009. Analyses of Self-potential Anomalies by Conventional and Extended Euler Deconvolution Techniques, Computers & Geosciences, Cilt. 35, s. 2231-2238.
• Sındırgı, .P Özyalın, Ş. 2019. Estimating the Location of a Causative Body from a Self-potential Anomaly using 2D and 3D Normalized Full Gradient and Euler Deconvolution, Turkish J Earth Sci., Cilt. 28, s. 640-659. DOI:10.3906/yer-1811-14
• Romeo, G. 1994. Seismic Signals Detection and Classification Using Artificial Neural Networks, Annali di Geofisica, Cilt. 37, s. 343–353.
• Röth, G., Tarantola, A., 1994, Neural Networks and Inversion of Seismic Data, J. Geophys. Res., Cilt. 99, s. 6753–6768.
• Zhang, Y., Paulson, K. V., 1997, Magnetotelluric Inversion using Regularized Hopfield Neural Networks, Geophys. Prosp., Cilt. 45, s. 725–743.
• Al-Garni, M. 2009. Interpretation of Spontaneous Potential Anomalies from Some Simple Geometrically Shaped Bodies Using Neural Network Inversion, Acta Geophysica, Cilt. 58(1), s.143–162.
• El-Kaliouby, H., Al-Garni, M.A. 2009. Inversion of Self-potential Anomalies Caused by 2D Inclined Sheets Using Neural Networks, J. Geophys. Eng., Cilt. 6, s. 29–34.
• Kaftan, İ., Şalk, M., 2009. Determination of Structure Parameters on Gravity Method by Using Radial Basis Functions Networks Case Study : Seferihisar Geothermal Area (Western Turkey). SEG Technical Program Expanded Abstracts, Cilt. 28(1), s. 991-994. DOI: 10.1190/1.3255917
• Kaftan, İ., Şalk, M., Şenol, Y. 2011. Evaluation of Gravity Data by Using Artificial Neural Networks Case Study: Seferihisar Geothermal Area (Western Turkey), Journal of Applied Geophysics, Cilt. 75, s. 711-718.
• Baddari, K., A¨ıfa, T., Djarfour, N., Ferahtia, J. 2009. Application of a Radial Basis Function Artificial Neural Network to Seismic Data Inversion, Computers&Geosciences, Cilt. 35, s. 2338-2344. DOI: 10.1016/j.cageo.2009.03.006
• Van der Baan, M., Jutten, C. 2000. Neural Networks in Geophysical Applications, Geophysics, Cilt. 65 (4), s. 1032-1047. DOI: 10.1190/1.1444797
• Qian-Wei, D., Fei-Bo, J., Li, D. 2014. RBFNN Inversion for Electrical Resistivity Tomography Based on Hannan-Quinn Criterion, Chinese Journal Geophysics, Cilt. 57(4), s. 1335-1344. DOI:10.6038/cjg20140430
• Levenberg, K. 1944. A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quarterly of Applied Mathematics, Cilt. 2 (2), s. 164-168. DOI:10.1090/qam/10666
• Marquardt, D.W. 1963. An Algorithm for Least Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, Cilt. 11, s.431-441.
• Jupp, D.L.B., Vozoff, K. 1975. Stable Iterative Methods for the Inversion of Geophysical Data, Geophysical Journal of the Royal Astronomical Society, Cilt. 42(3), s.957-976.DOI:10.1111/j.1365-246x.1975.tb06461.x
• Zellner, A. 1986. A Tale of Forecasting 1001 Series. The Bayesian Knight Strikes Again, International Journal of Forecasting, Cilt. 2, s. 491-494.
• Powell, M.J.D. 1985. Radial Basis Functions for Multivariable Interpolation. ss.143-167. Watson, J.C., Cox, M.G., ed. 1985. A Review, IMA Conference on Algorithms for the Approximation of Functions and Data, Royal Military College of Science, Shrivenham, England.
• Light, W.A. 1992. Some Aspects of Radial Basis Function Approximation. ss. 163-190. Singh, S.P., ed. 1992. Approximation Theory, Spline Functions, and Applications, NATO ASI Series, Kluwer Academic Publishers, Boston, MA, 256s.
• Kaftan, İ. 2010. Batı Türkiye Gravite ve Deprem Katolog Verilerinin Yapay Sinir Ağları ile Değerlendirilmesi. Dokuz Eylül Üniversitesi, Fen Bilimleri Enstitüsü, Doktora Tezi, 92s, İzmir.
• Drahor, M.G., Sarı, C., Şalk, M. 1999. Seferihisar jeotermal Alanında Doğal Gerilim(SP) ve Gravite Çalışmaları: Dokuz Eylül Üniversitesi, Mühendislik Fakültesi, Fen ve Mühendislik Dergisi, Cilt.1(3), s.97-112.